{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于机器学习的pyborker量化交易策略"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<diskcache.core.Cache at 0x239978a6a10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pybroker\n",
    "from pybroker.ext.data import AKShare\n",
    "from pybroker import ExecContext, StrategyConfig, Strategy\n",
    "from pybroker.data import DataSource\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import riskfolio as rp\n",
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sqlite3\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "import talib\n",
    "from pybroker.vect import cross\n",
    "\n",
    "#正常显示画图时出现的中文和负号\n",
    "from pylab import mpl\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "akshare = AKShare()\n",
    "\n",
    "pybroker.enable_data_source_cache('akshare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于机器学习算法在策略中的应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=sqlite3.connect(r'I:\\量化金融\\stock_2018.db')\n",
    "stock_daily1=pd.read_sql(\"select * from stock_daily where 股票代码<'003000.SZ'\",con=conn)\n",
    "stock_daily1[\"交易日期\"]=pd.to_datetime(stock_daily1[\"交易日期\"].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', '交易日期', '股票代码', '股票简称', '开盘价', '最高价', '最低价', '收盘价', '成交量(手)',\n",
       "       '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率',\n",
       "       '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)',\n",
       "       '总市值(万元)', '流通市值(万元)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1.columns=['index', \"date\",\"symbol\", '股票简称', \"open\",\"high\",\"low\",\"close\",\"volume\",\n",
    "       '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率',\n",
    "       '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)',\n",
    "       '总市值(万元)', '流通市值(万元)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func0(x):\n",
    "    return x.pct_change().shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1[\"return_s1\"]=stock_daily1.groupby(\"symbol\", group_keys=False).close.apply(func0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_slr0(symbol, train_data, test_data):\n",
    "    x1=train_data[['close','high','low','volume']]\n",
    "    y1=train_data['return_s1']\n",
    "    #model = DecisionTreeRegressor()\n",
    "    model = GradientBoostingRegressor()\n",
    "    #model = LinearRegression()\n",
    "    model.fit(x1, y1)\n",
    "\n",
    "    x2=test_data[['close','high','low','volume']]\n",
    "    y2=test_data['return_s1']\n",
    "    y_pred = model.predict(x2)\n",
    "    r2 = r2_score(y2, y_pred)\n",
    "    print(symbol, f'R^2={r2}')\n",
    "\n",
    "    return model,[\"close\",\"high\",\"low\",\"volume\"]\n",
    "\n",
    "def hold_long(ctx):\n",
    "    if not ctx.long_pos():\n",
    "        # Buy if the next bar is predicted to have a positive return:\n",
    "        if ctx.preds('slr')[-1] > 0.:\n",
    "            ctx.buy_shares = ctx.calc_target_shares(0.5)\n",
    "    else:\n",
    "        # Sell if the next bar is predicted to have a negative return:\n",
    "        if ctx.preds('slr')[-1] < 0.:\n",
    "            ctx.sell_shares = ctx.calc_target_shares(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyb_data_pe=stock_daily1[[\"date\",\"symbol\",\"open\",\"high\",\"low\",\"close\",\"volume\",\"return_s1\"]].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-21 00:00:00 to 2019-03-12 00:00:00\n",
      "000001.SZ R^2=-0.1531093981262832\n",
      "002594.SZ R^2=-0.242497593008264\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2019-03-13 00:00:00 to 2020-03-03 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 237) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 29% (71 of 237) |######                 | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 63% (151 of 237) |##############        | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 97% (231 of 237) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (237 of 237) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-03-13 00:00:00 to 2020-03-03 00:00:00\n",
      "000001.SZ R^2=-0.34570709077249995\n",
      "002594.SZ R^2=-0.4779307702234785\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2020-03-04 00:00:00 to 2021-02-23 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 237) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 21% (51 of 237) |####                   | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 51% (121 of 237) |###########           | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 80% (191 of 237) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (237 of 237) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2020-03-04 00:00:00 to 2021-02-23 00:00:00\n",
      "000001.SZ R^2=-0.4298399921222975\n",
      "002594.SZ R^2=-0.4268690708107139\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2021-02-24 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 237) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 29% (71 of 237) |######                 | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 67% (161 of 237) |##############        | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (237 of 237) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:06 \n",
      "\n",
      "Finished backtest: 0:00:12\n"
     ]
    }
   ],
   "source": [
    "model_slr = pybroker.model('slr', train_slr0)\n",
    "pybroker.register_columns('return_s1')\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=20,\n",
    "    windows=3,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result.orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trade_count</td>\n",
       "      <td>425.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>initial_market_value</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>end_market_value</td>\n",
       "      <td>110039.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_pnl</td>\n",
       "      <td>13141.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unrealized_pnl</td>\n",
       "      <td>-3102.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name      value\n",
       "0           trade_count     425.00\n",
       "1  initial_market_value  100000.00\n",
       "2      end_market_value  110039.32\n",
       "3             total_pnl   13141.45\n",
       "4        unrealized_pnl   -3102.13"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.metrics_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加入所有其他因子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'date', 'symbol', '股票简称', 'open', 'high', 'low', 'close',\n",
       "       'volume', '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)',\n",
       "       '市净率', '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)',\n",
       "       '流通股本(万股)', '总市值(万元)', '流通市值(万元)', 'return_s1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['open',\n",
       " 'high',\n",
       " 'low',\n",
       " 'close',\n",
       " 'volume',\n",
       " '成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor=stock_daily1.columns[4:].tolist()\n",
    "factor.remove('return_s1')\n",
    "factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_slr1(symbol, train_data, test_data):\n",
    "    x1=train_data[factor]\n",
    "    y1=train_data['return_s1']\n",
    "    #model = DecisionTreeRegressor()\n",
    "    model = GradientBoostingRegressor()\n",
    "    #model = LinearRegression()\n",
    "    model.fit(x1, y1)\n",
    "\n",
    "    x2=test_data[factor]\n",
    "    y2=test_data['return_s1']\n",
    "    y_pred = model.predict(x2)\n",
    "    r2 = r2_score(y2, y_pred)\n",
    "    print(symbol, f'R^2={r2}')\n",
    "\n",
    "    return model,factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#去掉stock_daily1中'股票简称'列\n",
    "pyb_data_pe=stock_daily1.drop(columns=['股票简称']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)',\n",
       " 'return_s1']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor[5:]+['return_s1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-23 00:00:00 to 2019-03-07 00:00:00\n",
      "000001.SZ R^2=-1.3687680810591587\n",
      "002594.SZ R^2=-0.6578603098779185\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      " 17% (41 of 232) |####                   | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
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      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n",
      "000001.SZ R^2=-0.24242033544728003\n",
      "002594.SZ R^2=-0.8807874670302995\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n",
      "000001.SZ R^2=-1.4877439327269264\n",
      "002594.SZ R^2=-0.5236108015544574\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2021-03-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  9% (21 of 232) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 39% (91 of 232) |#########              | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
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      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:02\n"
     ]
    }
   ],
   "source": [
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor[5:]+['return_s1'])\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=20,\n",
    "    windows=3,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trade_count</td>\n",
       "      <td>168.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>initial_market_value</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>end_market_value</td>\n",
       "      <td>37789.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_pnl</td>\n",
       "      <td>-60609.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unrealized_pnl</td>\n",
       "      <td>-1601.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name      value\n",
       "0           trade_count     168.00\n",
       "1  initial_market_value  100000.00\n",
       "2      end_market_value   37789.54\n",
       "3             total_pnl  -60609.23\n",
       "4        unrealized_pnl   -1601.23"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.metrics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'date', 'symbol', '股票简称', 'open', 'high', 'low', 'close',\n",
       "       'volume', '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)',\n",
       "       '市净率', '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)',\n",
       "       '流通股本(万股)', '总市值(万元)', '流通市值(万元)', 'return_s1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_name=[\"date\",\"symbol\",\"open\",\"high\",\"low\",\"close\",\"volume\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率', '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)', '总市值(万元)', '流通市值(万元)', 'return_s1']\n"
     ]
    }
   ],
   "source": [
    "factor_name=stock_daily1.columns[9:].tolist()\n",
    "print(factor_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_name[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_base=base_name[2:]+factor_name[:-1]\n",
    "#factor_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_slr1(symbol, train_data, test_data):\n",
    "    x1=train_data[factor_base]\n",
    "    y1=train_data['return_s1']\n",
    "    #model = DecisionTreeRegressor()\n",
    "    model = GradientBoostingRegressor()\n",
    "    #model = LinearRegression()\n",
    "    model.fit(x1, y1)\n",
    "\n",
    "    x2=test_data[factor_base]\n",
    "    y2=test_data['return_s1']\n",
    "    y_pred = model.predict(x2)\n",
    "    r2 = r2_score(y2, y_pred)\n",
    "    print(symbol, f'R^2={r2}')\n",
    "\n",
    "    return model,factor_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyb_data_pe=stock_daily1[base_name+factor_name].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-21 00:00:00 to 2019-07-01 00:00:00\n",
      "000001.SZ R^2=-0.722963022603436\n",
      "002594.SZ R^2=-0.5779074408662241\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 310) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 19% (61 of 310) |####                   | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 48% (151 of 310) |##########            | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 80% (251 of 310) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n",
      "000001.SZ R^2=-0.18297785024491597\n",
      "002594.SZ R^2=-0.4997214140101587\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2020-11-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 310) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 13% (41 of 310) |###                    | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 45% (141 of 310) |##########            | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 77% (241 of 310) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:02\n"
     ]
    }
   ],
   "source": [
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor_name)\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=20,\n",
    "    windows=2,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>symbol</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>exit_date</th>\n",
       "      <th>entry</th>\n",
       "      <th>exit</th>\n",
       "      <th>shares</th>\n",
       "      <th>pnl</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>agg_pnl</th>\n",
       "      <th>bars</th>\n",
       "      <th>pnl_per_bar</th>\n",
       "      <th>stop</th>\n",
       "      <th>mae</th>\n",
       "      <th>mfe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>long</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>1550.20</td>\n",
       "      <td>1535.46</td>\n",
       "      <td>31</td>\n",
       "      <td>-456.94</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-456.94</td>\n",
       "      <td>1</td>\n",
       "      <td>-456.94</td>\n",
       "      <td>None</td>\n",
       "      <td>-14.74</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>short</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>1535.46</td>\n",
       "      <td>1503.26</td>\n",
       "      <td>1</td>\n",
       "      <td>32.20</td>\n",
       "      <td>2.14</td>\n",
       "      <td>-424.74</td>\n",
       "      <td>1</td>\n",
       "      <td>32.20</td>\n",
       "      <td>None</td>\n",
       "      <td>-13.65</td>\n",
       "      <td>32.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>long</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>1503.26</td>\n",
       "      <td>1468.87</td>\n",
       "      <td>31</td>\n",
       "      <td>-1066.09</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-1490.83</td>\n",
       "      <td>1</td>\n",
       "      <td>-1066.09</td>\n",
       "      <td>None</td>\n",
       "      <td>-34.39</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>short</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>1468.87</td>\n",
       "      <td>1445.94</td>\n",
       "      <td>2</td>\n",
       "      <td>45.86</td>\n",
       "      <td>1.59</td>\n",
       "      <td>-1444.97</td>\n",
       "      <td>1</td>\n",
       "      <td>45.86</td>\n",
       "      <td>None</td>\n",
       "      <td>-20.20</td>\n",
       "      <td>22.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>long</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>1445.94</td>\n",
       "      <td>1547.47</td>\n",
       "      <td>31</td>\n",
       "      <td>3147.43</td>\n",
       "      <td>7.02</td>\n",
       "      <td>1702.46</td>\n",
       "      <td>2</td>\n",
       "      <td>1573.72</td>\n",
       "      <td>None</td>\n",
       "      <td>-23.47</td>\n",
       "      <td>101.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>short</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>1955.28</td>\n",
       "      <td>1931.21</td>\n",
       "      <td>1</td>\n",
       "      <td>24.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-56001.16</td>\n",
       "      <td>3</td>\n",
       "      <td>8.02</td>\n",
       "      <td>None</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>long</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>1931.21</td>\n",
       "      <td>1922.26</td>\n",
       "      <td>9</td>\n",
       "      <td>-80.55</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-56081.71</td>\n",
       "      <td>11</td>\n",
       "      <td>-7.32</td>\n",
       "      <td>None</td>\n",
       "      <td>-120.31</td>\n",
       "      <td>31.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>short</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>1922.26</td>\n",
       "      <td>1906.03</td>\n",
       "      <td>1</td>\n",
       "      <td>16.23</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-56065.48</td>\n",
       "      <td>1</td>\n",
       "      <td>16.23</td>\n",
       "      <td>None</td>\n",
       "      <td>-30.78</td>\n",
       "      <td>30.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>long</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>1906.03</td>\n",
       "      <td>1885.89</td>\n",
       "      <td>9</td>\n",
       "      <td>-181.26</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-56246.74</td>\n",
       "      <td>15</td>\n",
       "      <td>-12.08</td>\n",
       "      <td>None</td>\n",
       "      <td>-135.42</td>\n",
       "      <td>59.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>short</td>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>1885.89</td>\n",
       "      <td>1923.38</td>\n",
       "      <td>1</td>\n",
       "      <td>-37.49</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-56284.23</td>\n",
       "      <td>1</td>\n",
       "      <td>-37.49</td>\n",
       "      <td>None</td>\n",
       "      <td>-37.49</td>\n",
       "      <td>20.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type     symbol entry_date  exit_date    entry     exit  shares  \\\n",
       "id                                                                      \n",
       "1     long  000001.SZ 2019-07-31 2019-08-01  1550.20  1535.46      31   \n",
       "2    short  000001.SZ 2019-08-01 2019-08-02  1535.46  1503.26       1   \n",
       "3     long  000001.SZ 2019-08-02 2019-08-05  1503.26  1468.87      31   \n",
       "4    short  000001.SZ 2019-08-05 2019-08-06  1468.87  1445.94       2   \n",
       "5     long  000001.SZ 2019-08-06 2019-08-08  1445.94  1547.47      31   \n",
       "..     ...        ...        ...        ...      ...      ...     ...   \n",
       "162  short  000001.SZ 2021-12-22 2021-12-27  1955.28  1931.21       1   \n",
       "163   long  000001.SZ 2021-12-27 2022-01-12  1931.21  1922.26       9   \n",
       "164  short  000001.SZ 2022-01-12 2022-01-13  1922.26  1906.03       1   \n",
       "165   long  000001.SZ 2022-01-13 2022-02-10  1906.03  1885.89       9   \n",
       "166  short  000001.SZ 2022-02-10 2022-02-11  1885.89  1923.38       1   \n",
       "\n",
       "         pnl  return_pct   agg_pnl  bars  pnl_per_bar  stop     mae     mfe  \n",
       "id                                                                           \n",
       "1    -456.94       -0.95   -456.94     1      -456.94  None  -14.74   13.10  \n",
       "2      32.20        2.14   -424.74     1        32.20  None  -13.65   32.20  \n",
       "3   -1066.09       -2.29  -1490.83     1     -1066.09  None  -34.39   12.01  \n",
       "4      45.86        1.59  -1444.97     1        45.86  None  -20.20   22.93  \n",
       "5    3147.43        7.02   1702.46     2      1573.72  None  -23.47  101.53  \n",
       "..       ...         ...       ...   ...          ...   ...     ...     ...  \n",
       "162    24.07        1.25 -56001.16     3         8.02  None  -19.02   29.10  \n",
       "163   -80.55       -0.46 -56081.71    11        -7.32  None -120.31   31.90  \n",
       "164    16.23        0.85 -56065.48     1        16.23  None  -30.78   30.78  \n",
       "165  -181.26       -1.06 -56246.74    15       -12.08  None -135.42   59.32  \n",
       "166   -37.49       -1.95 -56284.23     1       -37.49  None  -37.49   20.15  \n",
       "\n",
       "[166 rows x 15 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.trades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义因子-20日均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于talib库的20日平均\n",
    "def func0(x):\n",
    "    return talib.SMA(x, timeperiod=20)\n",
    "\n",
    "stock_daily1[\"SMA20_close\"]=stock_daily1.groupby(\"symbol\", group_keys=False).close.apply(func0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['open',\n",
       " 'high',\n",
       " 'low',\n",
       " 'close',\n",
       " 'volume',\n",
       " '成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)',\n",
       " 'SMA20_close']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor=stock_daily1.columns[4:].tolist()\n",
    "factor.remove('return_s1')\n",
    "factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyb_data_pe=stock_daily1.drop(columns=['股票简称']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-23 00:00:00 to 2019-03-07 00:00:00\n",
      "000001.SZ R^2=-1.0609735892383951\n",
      "002594.SZ R^2=-0.749404328309192\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 232) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      "  9% (21 of 232) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 30% (71 of 232) |#######                | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 65% (151 of 232) |##############        | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 82% (191 of 232) |##################    | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n",
      "000001.SZ R^2=-0.8046091816253069\n",
      "002594.SZ R^2=-0.9483795987633561\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 232) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      "  9% (21 of 232) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 39% (91 of 232) |#########              | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 78% (181 of 232) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n",
      "000001.SZ R^2=-2.0076339697518653\n",
      "002594.SZ R^2=-0.9644746385509555\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2021-03-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 232) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      "  9% (21 of 232) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 34% (81 of 232) |########               | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 69% (161 of 232) |###############       | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 99% (231 of 232) |##################### | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:03\n"
     ]
    }
   ],
   "source": [
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor[5:]+['return_s1'])\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=20,\n",
    "    windows=3,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['open',\n",
       " 'high',\n",
       " 'low',\n",
       " 'close',\n",
       " 'volume',\n",
       " '成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stock_daily1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'date', 'symbol', '股票简称', 'open', 'high', 'low', 'close',\n",
       "       'volume', '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)',\n",
       "       '市净率', '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)',\n",
       "       '流通股本(万股)', '总市值(万元)', '流通市值(万元)', 'return_s1', 'SMA20_close'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['date', 'symbol', 'open', 'high', 'low', 'close', 'volume']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)',\n",
       " 'SMA20_close']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_name=stock_daily1.columns[9:].tolist()\n",
    "factor_name.remove('return_s1')\n",
    "factor_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_base=base_name[2:]+factor_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#factor_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyb_data_pe=stock_daily1[base_name+factor_name+[\"return_s1\"]].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'symbol', 'open', 'high', 'low', 'close', 'volume', '成交额(千元)',\n",
       "       '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率', '市销率',\n",
       "       '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)', '总市值(万元)',\n",
       "       '流通市值(万元)', 'SMA20_close', 'return_s1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyb_data_pe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-21 00:00:00 to 2019-07-01 00:00:00\n",
      "000001.SZ R^2=-6.012485786665197\n",
      "002594.SZ R^2=-0.4107363785072111\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 310) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 26% (81 of 310) |######                 | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 71% (221 of 310) |###############       | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n",
      "000001.SZ R^2=-0.42692847084190877\n",
      "002594.SZ R^2=-0.5831515332628479\n",
      "Finished training models: 0:00:00 \n",
      "\n",
      "Test split: 2020-11-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 310) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 10% (31 of 310) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 26% (81 of 310) |######                 | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 58% (181 of 310) |############          | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:02\n"
     ]
    }
   ],
   "source": [
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor_name+[\"return_s1\"])\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=30,\n",
    "    windows=2,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trade_count</td>\n",
       "      <td>119.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>initial_market_value</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>end_market_value</td>\n",
       "      <td>22532.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_pnl</td>\n",
       "      <td>-76729.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unrealized_pnl</td>\n",
       "      <td>-738.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name      value\n",
       "0           trade_count     119.00\n",
       "1  initial_market_value  100000.00\n",
       "2      end_market_value   22532.33\n",
       "3             total_pnl  -76729.09\n",
       "4        unrealized_pnl    -738.58"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.metrics_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_bb(stock_data):\n",
    "\n",
    "    high, mid, low = talib.BBANDS(stock_data, timeperiod=20)\n",
    "\n",
    "    x,y,z=talib.MACD(stock_data,fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "\n",
    "    dongliang=talib.MOM(stock_data, timeperiod=10)\n",
    "\n",
    "    rsi_6=talib.RSI(stock_data, timeperiod=6)\n",
    "    rsi_24=talib.RSI(stock_data, timeperiod=24)\n",
    "\n",
    "    up = talib.MAX(stock_data, 20)\n",
    "    down = talib.MIN(stock_data, 20)\n",
    "\n",
    "    return_30=stock_data.pct_change(30)\n",
    "    return_5=stock_data.pct_change(5)\n",
    "    return_10=stock_data.pct_change(10)\n",
    "\n",
    "\n",
    "    df=pd.concat([high,low,x,y,z,dongliang,rsi_6,rsi_24,up,down,return_30,return_5,return_10],axis=1)\n",
    "    df.columns=[\"b_high\",\"b_low\",\"MACD_x\",\"MACD_y\",\"MACD_z\",\"dong10\",\"rsi_6\",\"rsi_24\",\"up\",\"down\",\"return_30\",\"return_5\",\"return_10\"]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=stock_daily1.groupby(\"symbol\", group_keys=False).close.apply(compute_bb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1=stock_daily1.join(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1[\"close-o\"]=stock_daily1[\"close\"]-stock_daily1[\"open\"]\n",
    "stock_daily1[\"high-l\"]=stock_daily1[\"high\"]-stock_daily1[\"low\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['open', 'high', 'low', 'close', 'volume', '成交额(千元)', '换手率(%)', '量比',\n",
       "       '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率', '市销率', '市销率(TTM)', '股息率(%)',\n",
       "       '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)', '总市值(万元)', '流通市值(万元)',\n",
       "       'return_s1', 'SMA20_close', 'b_high', 'b_low', 'MACD_x', 'MACD_y',\n",
       "       'MACD_z', 'dong10', 'rsi_6', 'rsi_24', 'up', 'down', 'return_30',\n",
       "       'return_5', 'return_10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-23 00:00:00 to 2019-03-07 00:00:00\n",
      "000001.SZ R^2=-1.3898822492995633\n",
      "002594.SZ R^2=-0.528955938232482\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 43% (101 of 232) |#########             | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 78% (181 of 232) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-03-08 00:00:00 to 2020-03-02 00:00:00\n",
      "000001.SZ R^2=-0.8495384225532854\n",
      "002594.SZ R^2=-0.391792872151312\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 34% (81 of 232) |########               | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
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      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2020-03-03 00:00:00 to 2021-03-01 00:00:00\n",
      "000001.SZ R^2=-1.7132312033154142\n",
      "002594.SZ R^2=-1.4429180639007053\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2021-03-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 232) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
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      " 39% (91 of 232) |#########              | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
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      "100% (232 of 232) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:03\n"
     ]
    }
   ],
   "source": [
    "factor=stock_daily1.columns[4:].tolist()\n",
    "factor.remove('return_s1')\n",
    "pyb_data_pe=stock_daily1.drop(columns=['股票简称']).dropna()\n",
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor[5:]+['return_s1'])\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=20,\n",
    "    windows=3,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1=stock_daily1.join(stock_daily1.groupby(\"symbol\", group_keys=False).close.apply(compute_bb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_daily1[\"close-o\"]=stock_daily1[\"close\"]-stock_daily1[\"open\"]\n",
    "stock_daily1[\"high-l\"]=stock_daily1[\"high\"]-stock_daily1[\"low\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'date', 'symbol', '股票简称', 'open', 'high', 'low', 'close',\n",
       "       'volume', '成交额(千元)', '换手率(%)', '量比', '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)',\n",
       "       '市净率', '市销率', '市销率(TTM)', '股息率(%)', '股息率(TTM)(%)', '总股本(万股)',\n",
       "       '流通股本(万股)', '总市值(万元)', '流通市值(万元)', 'return_s1', 'SMA20_close', 'b_high',\n",
       "       'b_low', 'MACD_x', 'MACD_y', 'MACD_z', 'dong10', 'rsi_6', 'rsi_24',\n",
       "       'up', 'down', 'return_30', 'return_5', 'return_10', 'close-o',\n",
       "       'high-l'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['成交额(千元)',\n",
       " '换手率(%)',\n",
       " '量比',\n",
       " '市盈率(静态)',\n",
       " '市盈率(TTM)',\n",
       " '市盈率(动态)',\n",
       " '市净率',\n",
       " '市销率',\n",
       " '市销率(TTM)',\n",
       " '股息率(%)',\n",
       " '股息率(TTM)(%)',\n",
       " '总股本(万股)',\n",
       " '流通股本(万股)',\n",
       " '总市值(万元)',\n",
       " '流通市值(万元)',\n",
       " 'SMA20_close',\n",
       " 'b_high',\n",
       " 'b_low',\n",
       " 'MACD_x',\n",
       " 'MACD_y',\n",
       " 'MACD_z',\n",
       " 'dong10',\n",
       " 'rsi_6',\n",
       " 'rsi_24',\n",
       " 'up',\n",
       " 'down',\n",
       " 'return_30',\n",
       " 'return_5',\n",
       " 'return_10',\n",
       " 'close-o',\n",
       " 'high-l']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_name=stock_daily1.columns[9:].tolist()\n",
    "factor_name.remove('return_s1')\n",
    "factor_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtesting: 2018-03-19 00:00:00 to 2022-02-15 00:00:00\n",
      "\n",
      "Train split: 2018-03-21 00:00:00 to 2019-07-01 00:00:00\n",
      "000001.SZ R^2=-5.04927202684497\n",
      "002594.SZ R^2=-0.6617202437392751\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% (0 of 310) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--\n",
      " 10% (31 of 310) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 55% (171 of 310) |############          | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 80% (251 of 310) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train split: 2019-07-02 00:00:00 to 2020-10-30 00:00:00\n",
      "000001.SZ R^2=-2.511720570048893\n",
      "002594.SZ R^2=-1.312236315462128\n",
      "Finished training models: 0:00:01 \n",
      "\n",
      "Test split: 2020-11-02 00:00:00 to 2022-02-15 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      " 10% (31 of 310) |##                     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 45% (141 of 310) |##########            | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      " 80% (251 of 310) |#################     | Elapsed Time: 0:00:00 ETA:   0:00:00\n",
      "100% (310 of 310) |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating bootstrap metrics: sample_size=100, samples=10000...\n",
      "Calculated bootstrap metrics: 0:00:00 \n",
      "\n",
      "Finished backtest: 0:00:02\n"
     ]
    }
   ],
   "source": [
    "factor_base=base_name[2:]+factor_name\n",
    "pyb_data_pe=stock_daily1[base_name+factor_name+[\"return_s1\"]].dropna()\n",
    "\n",
    "model_slr = pybroker.model('slr', train_slr1)\n",
    "pybroker.register_columns(factor_name+[\"return_s1\"])\n",
    "config = StrategyConfig(bootstrap_sample_size=100)\n",
    "strategy = Strategy(pyb_data_pe, '2018-03-19', '2022-02-15', config)\n",
    "strategy.add_execution(hold_long, ['000001.SZ','002594.SZ'], models=model_slr)\n",
    "\n",
    "result = strategy.walkforward(\n",
    "    warmup=30,\n",
    "    windows=2,\n",
    "    train_size=0.5,\n",
    "    lookahead=1,\n",
    "    calc_bootstrap=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于seaborn的特征分析与可视化(回归)\n",
    "\n",
    "https://seaborn.pydata.org/archive/0.11/tutorial.html\n",
    "\n",
    "https://blog.csdn.net/qq_41185868/article/details/114041840\n",
    "\n",
    "https://blog.csdn.net/liujingwei8610/article/details/138870627\n",
    "\n",
    "https://blog.csdn.net/weixin_40787712/article/details/120695112\n",
    "\n",
    "https://zhuanlan.zhihu.com/p/165940283\n",
    "\n",
    "\n",
    "kind: Literal['scatter', 'kde', 'hist', 'hex', 'reg', 'resid'] = \"scatter\n",
    "\n",
    "\n",
    "* 分布特征\n",
    "\n",
    "* 相关性分析\n",
    "\n",
    "p值常见有三个标准一个是0.1，0.05，0.01；\n",
    "\n",
    "p值取值范围是0~1，不包括0和1，通常情况下，一般有三个判断标准一个是0.01、0.05以及0.1。在绝大多数情况下，如果p值小于0.01，则说明至少有99％的把握，如果p值小于0.05（且大于或等于0.01），则说明至少有95％的把握，如果p值小于0.1（且大于或等于0.05），则说明至少有90％的把握。\n",
    "\n",
    "在统计语言表达上，如果p值小于0.01，则称作0.01水平显著，例如，研究人员分析X对Y是否存在影响关系时，如果X对应的p值为0.00（由于小数位精度要求，展示为0.00），则说明X对Y存在影响关系这件事至少有99％的把握，统计语言描述为X在0.01水平上呈现显著性。如果P值小于0.05(且大于或等于0.01)，则称作在0.05水平上显著。\n",
    "\n",
    "r 相关性大小意义：\n",
    "\n",
    "|r|=1时，完全相关；\n",
    "\n",
    "|r|≥0.8时，高度相关；\n",
    "\n",
    "0.5≤|r|＜0.8时，中度相关；\n",
    "\n",
    "0.3≤|r|＜0.5时，低度相关；\n",
    "\n",
    "|r|＜0.3时，基本不相关；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['open', 'high', 'low', 'close', 'volume', '成交额(千元)', '换手率(%)', '量比',\n",
       "       '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率', '市销率', '市销率(TTM)', '股息率(%)',\n",
       "       '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)', '总市值(万元)', '流通市值(万元)',\n",
       "       'return_s1', 'SMA20_close', 'b_high', 'b_low', 'MACD_x', 'MACD_y',\n",
       "       'MACD_z', 'dong10', 'rsi_6', 'rsi_24', 'up', 'down', 'return_30',\n",
       "       'return_5', 'return_10', 'close-o', 'high-l'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily1.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "tezheng=stock_daily1[stock_daily1.columns[4:]].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tezheng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['open', 'high', 'low', 'close', 'volume', '成交额(千元)', '换手率(%)', '量比',\n",
       "       '市盈率(静态)', '市盈率(TTM)', '市盈率(动态)', '市净率', '市销率', '市销率(TTM)', '股息率(%)',\n",
       "       '股息率(TTM)(%)', '总股本(万股)', '流通股本(万股)', '总市值(万元)', '流通市值(万元)',\n",
       "       'return_s1', 'SMA20_close', 'b_high', 'b_low', 'MACD_x', 'MACD_y',\n",
       "       'MACD_z', 'dong10', 'rsi_6', 'rsi_24', 'up', 'down', 'return_30',\n",
       "       'return_5', 'return_10', 'close-o', 'high-l'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tezheng.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    983617.000000\n",
       "mean         49.295826\n",
       "std          17.935456\n",
       "min           0.138213\n",
       "25%          36.210643\n",
       "50%          48.880854\n",
       "75%          62.159684\n",
       "max          99.863548\n",
       "Name: rsi_6, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tezheng['rsi_6'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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seOYZdSLeyy9DVi4jchcMaiKN/PADkJsrxlrtSE5mULd1Fvivf30SXbrUydf0rbe4bzW5DwY1kUYWLlS7um+9tZTd3k6YBT5zZiUSEo7Kx1OmBGDHjv3cVYvcAoOaSAMiO/71L3XWU3X1aZjNZq2b5BazwHv3rpWV3aqrvfHUU8HcVYvcAoOaSKPZ3vn56mzvrl21bo37MBqBfv2K5fHRo4nw8+OuWuT6GNREGs72TkvjbG9n69q1HAEBFlRVmWQNdSJXx6Am0qDbm7O924+XF9CrV6E8/vHHIG7YQS6PQU2kwWzvM2eAoCDO9m4vGRlF8PGxoaTEG+vXq5XfiFwVg5qog4jZx2IW8pdfVsrHI0eKbm+tW+WefHzs6NmzVB5Pn+6DvDzO/ibXxaAm6iBi9vHLLxdg7lxv+XjUKF5Nt6frry+Fl5cd+/cH4Q9/qODsb3JZDGqiDlRTE4+KCl/4+SkYNozls9pTQIAN6ekX5PGhQ0laN4eo1RjURB3o+HF1FvLQoTUICOBs7/bWu7eYVKbg5Ek/HD3KcQZyTQxqog50/Lg6senXv2a3d0cIDa1FcrI6J+DjjwO1bg5RqzCoiTrIyZNeuHDBG0ajIieSUcfo3Vvd5/urrwLAYWpyRQxqog6yZo3a7Z2QUIuwMHZ7d5SYmBp06WKBxWLAzJlat4bo2jGoiTrIqlVqt3daWrWcgSxuitjrktpd//5q9/cHHwBVVVq3hujacHYFUQcQBU727PGRE5tiY3MxdarYhKMKgYFBWjfNI3TtWoPExDrk5JjwxRfAH/6gdYuIWo5X1ETtTBTa+OILtfhGbKwFAQF1cqenwMAorZvmMQwGO37727PyeNo0wKZuXEbkEhjURO1MdHHPmuUljxMTy7RujsfuV3306G6EhNhx/DiwfLnWLSJqOQY1UTsrKTHg7Fm1i9uxVIg6XqdOYfg//0fd9/udd7RuDVHLMaiJ2tmmTX5QFAPCwqoRHFyndXM8lqLYcffdp+Djo2DbNsgbkStgUBO1s2+/9ZX37PbWVnV1MebMKUVKSpF8/PLLNdyog1wCg5qonYgQyM3NR2ammO3NoNYDMYmvTx+1rOiGDX7YuFENbSI9Y1ATteMksokTK1BWZoKvrw3R0er4KGlfVjQlRZ0rMGsWl8eR/jGoidrR2bOx8j4+3gwjf9t0o2/fYnm/dKk/srO1bg1R8/jWQdSOsrPVamSJibya1pOoqFokJtbAZjPg1VfNHKsmXWNQE7WTvDwvFBV5w2BQkJDAoNabXr1y5P2cOf44eFCMWxPpE4OaqJ1ne3fubIafH6/Y9CY2thJRUeKq2ohPPuEWmKRfDGqidiJmFQuc7a1PBgPQp486Vj1nTgCqq7VuEVHTGNRE7UC86W/b5liWVa51c+gXiEpxoghNSYkX5s7VujVETWNQE7WDjRuBmhojgoJsCA/npZpeiZn4ffuq8wemT1eQl5eP/Px8Ti4jXWFQE7WDlSvV+9TUGtnFSvp1/fVVCAiw49AhAx5/3IxXXlH3CifSCwY1kZMpCrB6tXqcklKjdXPoKkQxmrvvViuUHT8ej6CgGK2bRNQIg5rIyX7+WayfBry9xbIsi9bNoRZsgVlZmSuPT5zwQ1UV3xZJX/gTSeRk69ap9wMGWOROTaR/8fGdEBUlCp8YcPiwv9bNIWqEQU3kZGvXqve3316rdVPoGvTooXZ/HzgQIIcviPSCQU3kRBaLmPGtvstff30eFL7ju4y0tBKYTHaUlHjjxx/VpXVEesCgJnKiHTuAykoD/PysWLkyB2YzS4e6Ch8fO1JTK+Tx11+z+5v0g0FN1A7d3snJVgQFRWndHLpGXbuqQb1ihR+sVq1bQ6RiUBM5iSiSsXKlOss7KYnLslxRbKxYU22TlcrWr9e6NUQqBjWRkxw6VIj9+73lcVRUidbNoVZWKuvWTa0kN3++1q0hUjGoiZxkyxaxW5YBERHqVRm5ph491KBesgSoqtK6NUQMaiKndHmL+tBr1qj1oePjuQmHK4uJqUVsbC3EPMBVq1jzm7THoCZqI1EX+uWXC/Dtt+qexvHx6oQkck1VVYUIDj4vjxcs4FwD0p7ugnr27NlISEhAQEAAbr/9dpw8eVI+f/DgQQwcOBBhYWF4/vnnG61P3bx5MzIyMhAZGYmpU6c2+nqLFy9GUlISYmNjMZ+DTtROamriUV3tJ9fhxsRUat0caqNu3ez1e4pz9jdpTVdBfeLECbz66qtYunQpsrKykJaWhkceeQS1tbUYM2YM+vfvj127duHw4cP4/PPP5d85f/48xo4di3HjxmHHjh2YO3cuNoo9Bi+G+/jx4zF58mSsXbsWL774Io4eParxd0nu6PRpMT4NdOlSBS8vFjlxdZ0718Df34ayMiM2b9a6NeTpdBXUe/bswaBBg3DDDTcgMTER//f//l/8/PPPWL16NcrKyuTVsgjvN954A5988on8OyKYxdWyCOP09HQZxo5z4up82LBhmDBhAnr16oWJEydizpw5Gn+X5I5OnfKT9/HxnH3kLrO/09LUSWVz54oa4ByrJu3oKqh79uyJzMxM7N27VwbzrFmzcMcdd2Dfvn0ywEV3uNC7d295VS2IcyKMDRc3/b3xxhuxe/fu+nPDhw+v//oNzzVFXLmXl5c3uhFdTXW1AXl5asnJhARWInMX8fHqntSLFnkjP5/7U5N2dBfUDz74IPr164fQ0FDZlf3OO+/IwExJSan/cyKUvby8UFJScsW54OBgOQNXaO5cU6ZMmYKQkJD6mxgrJ7qa77/3gc1mQFBQLUJCOKDpLmJjK+DtbYfZ7IN9+9T18UTw9KDeuXMnli9fju+//x6lpaVy3HnUqFEwmUzw9VXHAB38/MS+sVVXnHM8LzR3rimTJk2SV/KOW26uukctUXM2bfKtn+19sWOH3IDJpNT3kKxZow5tEMHTg1rMyn7ooYdw0003ySva1157TU4wCw8Pl5PGGqqoqICPj88V5xzPC82da4oIdXHV3fBGdDWbNzuCmkMl7iY5WZ3Bz6AmLekqqMWEjcLCwkbB6rhqFt3gDtnZ2XI8WQSxWLLV8JyYkBYXFyePmztH5Ayi0+X4cW8YDAri4rh+2t0kJpphNCr4+WdvZGVp3RryVLoK6iFDhuDrr7/GtGnTMG/ePNx7772IiYnB008/LcebP/vsM/nnxKzvkSNHynFqsTRr27Zt2LBhA6xWK9566y3ceeed8s898MADWLBgAQ4cOIDKykrMmDGj/hyRM2zYoN7HxFjh68uyoe649WViYq08/uYbrVtDnsoEHRHBeuTIEUyfPh1nz57F9ddfjyVLlsDb21sutRJj1qLYidFoxKZNm+TfEUVORLCLseygoCA5Cc2xxrpPnz545plnMGDAADk+LZZvPfnkkxp/l+ROMjPVe8ebObmfrl1r5PI7Ufv7v/9b69aQJ9JVUIvZ3GI9tLhdTlw5i/FqsbxKLNWKiIioP/fEE0/IK2VRJEVclYvAdnj99ddl0ZO8vDwMHTq02TFqomths9mxYYMobuKF+HiWmnRXaWk1sudk505RLlb0nmjdIvI0uur6vhrRDT569OhGIe0glmH9+te/bhTSDZd9ifXYDGlypm3bzqOgwAtGow0hIaVaN4faSUBAHa67Tp39vWIFC59Qx3OpoCbSk23b1NneMTFiwiPLhrors7kQRmORPF6yhEMc1PEY1ESttH27b31hDHJvXbteWopXy6ymDsagJmoFUfp5+3Z1KIVB7f4iI2sRGGiD2WzEd99p3RryNAxqolY4eBAoLvaSJSajorgRh7sTFedSUtQJgwsWVHKTDupQDGqiNizLiouzcFtLD9Gli2OTDhPOnuUmHdRxGNRErXBxy3MkJFi0bgp1EFF5zmi0o7zcDydOeGndHPIgDGqia1RXJzbiUI8TEjizyJOqlHXpou5RvWEDa39Tx2FQE7WQGJcU26SuW3ceYqvy4GA7OnfmtpaeJClJXU/97bcMauo4DGqiFiooKMArrxTg5ZfVq+h+/crlZhzkWZt0CDt3+qCsTOvWkKdgUBNdg6CgGJw9q25/Wll5Fmaz+sZNniE42IrwcCvq6gxYt07r1pCnYFATXQObTVxZB8rjlBReTXuilBS1R2XFCq1bQp6CQU10Dc6d80ZdnZfc0jIsjBtxeKLUVPX/fdUq9YMbUXtjUBNdg7w8tWyomP0rimCQ5+nSpQZBQXUoKgJ++IGFT6j9MaiJrsGZMz71G3GQZ6qpKUR4eIk8/te/OEeB2h+DmqiFRNXI/HxHUKvrackzpabWyfu1a73kkj2WFKX2xKAmaqGsLBNqa40wmWxykwby9GVaCo4eDcDzz1+QS/eI2guDmqiFxNpZITraDCN/czyan59d/hwIhYUJWjeH3Bzfboha6IcfHBPJKrVuCulAQkK5vM/OVn8uiNoLg5qoBRRFBLVjfJpBTaL7Wy1Ndvq0L2o5EkLtiEFN1AInT4o11F4wGhV07syZvgRERFQjIKAOdXXG+t4WovbAoCZqge++U++jo60wmViRjCDX0SckODbpYFBT+2FQE12FWHqzdq36hhwXxz5OusQR1OvXm7hMi9oNg5roKsTSm1WrvORxZKQ6gYhIiI+vkjuonT7th2efLeYyLWoXDGqiqygsNKK8XOw/rCA6moVO6BIfH3v95MJz5+K1bg65KQY10VU4ZnuHh1fD15ddm9T07O/sbPFhjsj5GNRELSx0wmVZ1Nx66txcX1RXc6cWcj4GNdFVsNAJNUdsdxoUZIXNZsD27eqHOiJnYlATNaO0FDh82CSPGdR0tWVamZns/ibnY1ATNWP7dlGVzICQkDpZ3ILolzfpUNdTiyp2RM7EoCZqxpYt6rsu109Tc2Jjq+DlpSA314SjR7VuDbkbBjVRMzIzLfI+MrJC66aQjnl7K4iPVz/MrVqldWvI3TCoiX5BdTWwb586OahLF66fpualpDCoqX0wqIl+wc6dgMVigJ9fDYKDrVo3h3QuJaVG3m/ZAlSwA4aciEFNdJWNODp3LpEze4maExpqQ3JyHaxWMWSidWvInTCoiX6BuDISoqJKtG4KuQDxYW74cPWqmt3f5EwMaqImiKuibdvU4+joC1o3h1yAotjRr5+6Kcfy5XXIy+NuWuQcDGqiJuzaBVRViapTNoSEsNAJXZ3ZXIitW8/By8uGs2dN+K//KuVuWuQUDGqiJmzcqF4J9esn6jizggW1TEhIFOLi1A92BQXcTYucg0FN1IR169T10+XlBagW67SIWighwbGbllojnqitGNRETYxPO3bMSk7m1TS1bjet/HwflJVxuQC1HYOaqInx6epqI/z8bHJnJKJrERxsQWhorawRv2ULr6qp7RjURJfZtEm9j4+3cP00tWmTDu6mRc7AoCa6zObNl4KaqDUc215u3OgLrtCitmJQE102Pr11q3qckMAds6h1YmJq4O1tx/nzXtizR+vWkKtjUBM1sHu3WA8rykHaERnJ/aepdcSWl4mJ6ge91au1bg25OgY1URPj0zffXMvxaWoT7qZFzsKgJmoyqDk+Tc7ZTev774GiIq1bQ66MQU3UaHxaXTfdrVs+FIVrqKn1goLqkJ5eBfFjtG6d1q0hV8agJmo0Pm2Ar28dli3LgVkMVhO1ofa3wZAjj9n9TW3BoCa6yLGHcEKCFUFBUVo3h9xAQkKVvF+1ygarleu0qHUY1EQXrV+v3iclcVkWOUdg4Cl4e9ehpMQLa9Zwu1RqHQY1EYDKykv7TzuW1RC1ldGoICFB3dSFVcqotRjURBerkYnJZImJdQgNtWndHHLLcqKs+02tw6AmatDtfdttXD9NzhUfrwb1/v0+KCjQujXkihjURHL/aXUpVp8+57gsi5wqIMCG6Gh1Xf6aNVq3hlwRg5o8Xm4ucOSIAQaDgp07s7ksi5wuOZlVyqj1GNTk8Rzd3jExVoSFhWndHHLjKmWi8EkdS8jTNWJQk8fjsixqb+qHQBvKyoAdO7RuDbkaBjV5NLFXMIOa2pvRCAwbxu5vah0GNXk0sVfwhQuiLrMdMTHciIPah6LYccMN5+Tx0qUW5Ofnwy4+JRK1AIOaPNq6deqbZf/+5bI4BVF71f3eu1eszVJw5IgPnn/+Agq4VotaiEFNHm3FCqu8r6rK52xvalfh4RGIjlZ/xgoLE7RuDrkQBjV5LJHLP/7oI49TU3k1Te0vIaFc3mdns0oZtRyDmjzWli2ibKgBwcF1CA7mRDJqf4mJZfI+J8cXtfyRoxZiUJPHEmtaHbO9WTaUOkJERDX8/etgtRqxc6fam0N0NQxq8lhclkUdTXwgTEhwbNLB3bTIxYP6hRdewJgxY+ofHzx4EAMHDpSVo55//vlG9Zg3b96MjIwMREZGYurUqY2+zuLFi5GUlITY2FjMnz+/Q78H0q/cXDsOHRJvnGIbQrVqFFFH4G5a5BZBvX//fsyaNQvvvfeefFxbWytDu3///ti1axcOHz6Mzz//XJ47f/48xo4di3HjxmHHjh2YO3cuNm7cWB/u48ePx+TJk7F27Vq8+OKLOHr0qKbfG+nDV1+pY4Xh4WWw2Sq1bg55kLi4KvkB8eefvZGdrXVryBXoLqhFEYDHH38cf/rTn5CamiqfW716NcrKyuTVclpaGt544w188skn8pwIZnG1LMI4PT1dhrHj3OzZszFs2DBMmDABvXr1wsSJEzFnzhxNvz/Shy1b1KuZxMRqrZtCHsbX147YWLW4zurVWreGXIHugvrDDz/EgQMHkJycjGXLlsFisWDfvn0YNGgQAgIC5J/p3bu3vKoWxDkRxoaLs4FuvPFG7N69u/7c8OHD6792w3NNEVfu5eXljW7kfkRBqO++U4M6Pr5C6+aQB0pJYTlRctGgrqysxEsvvSSvpE+fPo1p06bh1ltvlYGZkpJS/+dEKHt5eaGkpOSKc8HBwbI8n9DcuaZMmTIFISEh9beEBBYlcEf79wNFRV7w9rajc2cWOSHtdtPKzASq2alDrhTUX3/9tawOJcaYX3nlFaxfvx4VFRX49NNP4evbeOKFn58fqqqqYDKZGp1zPC80d64pkyZNkl3sjluu2KiY3Ha2d0KCBV5eLHRCHS8ysg5duthkSG/erHVrSO90FdRnzpyRXdxi9rYjaEU3d2lpqZw01pAIcB8fH4SHhzc653heaO5cU0Soi6vuhjdyL2IOxPLlardjYiJne5M2xEjd8OHqzx+7v8mlgjo+Ph7Vl/UDiS7w6dOnyxndDtnZ2XI8WQSxWLLV8NyePXsQFxcnj5s7R54pO7sA27d7y+POnUu0bg55sBEj1A+MK1eK3bW0bg3pma6CevTo0XKSmJhQJq6uZ8yYISeE3X///XK8+bPPPpN/Tsz6HjlypBynFkuztm3bhg0bNsBqteKtt97CnXfeKf/cAw88gAULFsjJaWL8W3w9xznyTDt3+sJmMyIw0IKQEHVDDiIt3HprLby9gZMngePHtW4NuV1QizAUodhQZmYmhg4d2qbGREREYNWqVfjiiy/QrVs3uY564cKFclKXWGollleJbvGlS5fif//3f+XfEY/FpLNRo0YhOjparpP+61//Ks/16dMHzzzzDAYMGCCvpEWwP/nkk21qI7nHsqz4+HKWDSVNBQYqcLxlsvubnB7UYtLV5V3UPXv2xPfff4+2Gjx4sOyuFpO+Tpw4UV+dTFw5i8cixI8cOSL/PYcnnnhCBrRYUy2KpYjAdnj99ddll7eoULZ9+3b4+/u3uY3kujZvVoM6Lo7Lskg7imKX+1HfcotaeIdBTc0x4RpsEdsNyR8yRXY3BwYG1j9et24devTogfYUExMju8ebIpZhNVyK1ZAI9YbBTp6poAA4csS7QVCz1jJpw2wuxNSpZlitZwCEyJnflZVAUJDWLSOXD+qHH364fh2zuIo1GtULcnHftWtXfPnll+3TSiInzPZetEhcvYShc2eL3MGISEuBgdEIDAxCYmIdcnJMck312LFat4pcPqjFbGtHMIsJWly+RK5CdDO+/77a7d2lC2t7k17YMWhQMXJyOmPlSgVjx3LiBDlpjFrMnPYW0xWJXIRY/pKfHyqP4+NZjYz00wVeWFgoj1eutHGZFjkvqMUmGZyURa7k6FETzGYveHnZERPDQiekH6mpvrJCXl6eCRe3MCBqe1B/8803ctMMsdzJcRPd4eKeSM+zvWNjK1g2lHTFZBJ7onOTDnJyUIuJZGL/56ysLJw8eVLexPi1uCfSI8duWVyWRXqUkqIud12ypFZOfCRq9WSyhh577LH6/aKJ9KymBtixw6e+0EkbfuyJ2kV0dJ5ckfD99z44dqwAPXp00bpJ5OpX1K+99hqeeuopXLhwwfktInKy7dtFWIuyoTaEhXF8mvQnOFiUtLVAUQz1vT9EDq26tBAVwA4ePIjExERkZGQ0WqYlSokS6cm6dep9UlIty4aSbiUmmnHggA8yM33x2GNat4ZcPqgfeeQR57eEqJ33n05MVCfsEOlRQoII6jBkZvpBDFNfrCdF1LqgdlQoI9K7c+fs2LNHXEYbuP806VqXLtXw9rajsNAL69efxx13RNRXfyTP1qqfAsdSrKZuRHry9delctwvNFTM9uaMb9IvsWwwLk5MdgT+8herrKZH1OorakcpUUHscrVr1y68/fbb+Mtf/sJXlXS5rWViYpXWTSG6qsTEMpw6FYozZyIBFGndHHLloE5KSmr0WEwou+uuu+SWlP/2b//mrLYRtYkox7h5s1+DZVlE+g9q4dw5HxQWGhEbq3WLSA+cNgAiSoqyq4b05OhR4OxZMSSjICaGG3GQ/gUE1CEqSp1LIWZ/E7X6inrYsGFyq0sHUUnn8OHD+NWvfsVXlXS3LCsuziLLNBK5gsTESpw/74f16/3w5z9r3Rpym+VZIrTj4uIwfPhwZ7WLqE3Eh8flyy0A/Djbm1xKUpIZu3dHyvkVoqqenzp6Qx6sTcuzxPZsOTk5csw6KirK2W0jarWcnAJs2hQtjzt3LtW6OUQtFhFRK6void3eNm8W2wpr3SJyyTHq8vJy3HfffejSpQuGDBmCmJgYPPjgg/J5Ij346Scf1NV5wc/PKt/4iFyFGFVMTVV7gVas0Lo15LJB/eSTT8quxdzcXFRXV8v7uro6+TyRnra1jI+vYNlQcjkNg1qsXiDP1qqu79WrV2P37t2Ivbh2QNxPmzYN/fv3d3b7iNq4rSV7ecj1JCZa4Our4NQpAw4fBq67TusWkctdUYvNOC7ffEM8vnx9NZEWiouBvXu966+oiVyNt7eCwYPVIRt2f1Orrqjfe+89jB49GgsXLpR7Up84cQLbt2/HqlWrnN9ComskPkOKsqEREVYEBloBcD0quRZFsWPgwEJkZiZixQoFL7zA8RtPdk1X1GJceseOHejUqROOHDmCoUOHYt26dfI+KytLTiwj0tO2lkSuyGwuRFZWQf1+6gcPnpXvv+SZWhzUBw4cQPfu3WWZ0BUrViA+Ph6TJk1CQEAA3nzzTdxyyy2y6AmRlsTEG0dQc1tLcmXR0aEID6+G3W7A00/XsPKjB2txUD/22GPyJtZOT548uf75/fv3o6SkBOPGjcOECRPaq51ELXLsmB2nTwMmkx3x8Qxqco/a3/n5ak0A8kwtDuqDBw/KtdJN7Y8qtrf8/e9/L0ObSEtffaXO8o6IKIXFwvre5NqSktSgPnXKD1Yx3YI8UouDeuTIkfjTn/6EoqIrt14zm8146aWXMHjwYGe3j+iabNyo1ltMSqrWuilEbRYVZYafXx1qa4348UcfrZtDeg/qTz/9FDabTVYjE2PVYuLY7bffjr59+yIyMlJecX/yySft21qiZtTWAtu2qW9mCQlcP02uT3RgJiSY5bHYpIM8U4uXZ4WHh8tJZGIplpj5nZ+fD6vVitDQUBnWYjJZwx21iDra1q1AdbVR1kkWk3CAIK2bROSUTTqOHw/Bt99ymaGnuuZ11GlpafJGpDerV6v3ycm1LBtKbiM+vgpGo4ITJ7zx889A165at4hcojIZkR6tWaPeJydzW0tyHz4+drmnurBypdatIS0wqMktnD5tx6FDYkxP4f7T5Ha4m5ZnY1CTW1i0SJ08Fh5eCpuNy7LIPYNa7E9dwfL1HodBTW5h40Z1og2XZZE7Cg21IiGhRq6lXruWpUQ9DYOaXJ5483Jsa8llWeSutb8DAy/I48WLObTjaRjU5NLERgUrVxahosIIPz8bIiOrtG4SUbtIS1OvpMUyLe7P4VkY1OTSxEYFr76q1laMizPLAhFE7igmplrOAC8q8sLu3Vq3hjoS39bI5eXlRch7RwUnInfk5XVp61bO/vYsDGpyaefPG1FY6FNfGILInaWkqJMllyyxcH9qD8KgJpe2ebM6iSwiogoBATatm0PUrjp3PiN2XceBAz7Yu7dQ6+ZQB2FQk0vbtImzvclzBATUoXNnddZ3ZiY36fAUDGpyWTZbw6BW9+0lcneJiepcjA0buEmHp2BQk8v66SegpMRLzoSNjuZEMvKsoN6yxRc1XFLtERjU5PKbcCQm1nJZFnmMiIhauZWr2NL1q68ucFKZB+DbG7kk8ea0bJm6oxB3yyJPIrZwTUgolsdTpthkLQFybwxqcklZWeewe7e3PO7cuUTr5hB1qMREdfJkbm44FEXr1lB7Y1CTSxLjc4piQFhYNYKC6rRuDlGHiourgJeXHeXlJhw9atK6OdTOGNTkkjZtUpemxMdzWRZ5Hm9vO2Jj1QI/a9ZwmZa7Y1CTyxFdfVw/TZ4uJUXdd51B7f4Y1ORyDhwAzp3zgslkR0yM+mZF5GmSkswwGNQqZadPa90aak8ManLZZVkJCRaYTJxJQ57J39+G2Fh15cM332jdGmpPDGpyuWVZS5eqOwhxWRZ5uq5d1d+BJUu0bgm1JwY1uZSffz6H77/nsiyihkH93XdiJzmtW0PthUFNLmX7dh/Y7UYEB9cgJMSqdXOINBUSYsN111khipMtX651a6i9MKjJpWzcyGVZRA6KYsett6qX0l9/zfka7opBTS61LGvjRi7LInIwmwuRm3tWHm/YAFRyEYRbYlCTyzh2TJRMNMHLS0FsLN+RiIS4uE4ICalDba0B8+YVc5MON8SgJpdblhUXZ5GVmYhI3aQjKemCPH77bXCTDjfEoCaXsXq1OgbHZVlEjSUnl8r7nJxQ1KqrF8mNMKjJJVRXA5s3q8dclkXUWHS0GQEBdbBYjNi6VZ3HQe6DQU0uYcsWoKbGgICAaoSFqdWYiOhS93dKSoU8XrnSX+vmkJMxqMmlxqe7dCmSb0pE1PQmHWvX+sHKEgNuhUFNLhbULL9E1JSYmGoEBNhQWmrExo1at4aciUFNunfypB1ZWZDLsqKji7RuDpEuGY1AWlq1PF60iMVP3AmDmnRv4UK1uEl4eClsNnUcjoiulJCgFj/5+ms76uq0bg15RFDfdddd+Pzzz+Xx5s2bkZGRgcjISEydOrXRn1u8eDGSkpIQGxuL+fPnNzo3c+ZMREdHIzU1FZmZmR3afnKOTZvUWaxJSerVAhE1LTa2Ar6+VhQXe8kJmOQedBvUc+fOxdq1a+Xx+fPnMXbsWIwbNw47duyQ5zZeHIQ5ePAgxo8fj8mTJ8s//+KLL+Lo0aPynHj83HPP4aOPPsI///lPTJgwARcuqIUByDVYLKhfbpKQUKZ1c4h03/0dH68ODy1erHVryK2Duri4GM8++yy6d+8uH4tgFlfLIozT09NlGH/yySfy3OzZszFs2DAZwr169cLEiRMxZ84cee6DDz7Aww8/jHvuuQe33HKLvF/CjVtdyvbtop6xUU6SiYzkFTXR1cTHF8r7r76ywWplBT93oMugFiF93333YdCgQfLxvn37ZBgbLq7LufHGG7F79+76c8OHD6//uy0915Ta2lqUl5c3upE+ZnsnJdVyWRZRC4SEnICPjxWFhV5YsaJY6+aQOwa16NL+9ttv8dZbb9U/JwIzJSWl/nFwcDDy8/PbdK4pU6ZMQUhISP0tISHB6d8ftS6ok5NZF5GoJYxGBcnJVfJ45Up1W1hybboK6pqaGvz+97+XXdadOnWqf95kMsHX91JZPD8/P1RVVbXpXFMmTZqEsrKy+ltubq7Tv0dqOfGZat8+UXVJkVfURHRtxU9ElTJupuX6TNCRv/3tbxg4cCBGjx7d6Pnw8HA5ocyhoqICPj4+bTrXFBHqDYOdtLV6tXiHMSIjowr+/jaYzVq3iMg1xMdXwcfHjoICL/zwA3DzzVq3iNwmqOfNmyeDNTQ0VD4WV78LFy6Ux2IymMOePXsQFxcnj0Wwi5ng//mf//mL50aMGHHFOdK/JUvEVbQ/vLzOwmzmZQFRS4niQKmp1cjKCsTnn1fippsCYBRTwskl6ep/7rvvvpPLrfbu3StvYknWq6++ipycHGzbtg0bNmyA1WqV49d33nmn/DsPPPAAFixYgAMHDqCyshIzZsyoP/fggw9i1qxZyMvLw7lz5+RMccc50jdRq3jLFrV3Iy2NlRuIrlViorov9bx53jh7lntUuzJdXVHHx8c3ehwUFCQLnIjbtGnTMGrUKPmcuOJ2FELp06cPnnnmGQwYMECOQYvlW08++aQ8N2bMGCxatEg+J4gr6/vvv1+D74yu1datYqjCKLu8o6LEvIIgrZtE5FLi48thMtlRWemLffu8wc5E16WroL6cI4yFJ554Ql4NZ2VlYciQITKwHV5//XVZ9ERcOQ8dOrR+HFos5xJrqp9++mmYzWZ5zrHEi/Rt1Sr1PiWFy7KIWsNkUpCYaMbJk53k7O9Ro7RuEbllUF9OLLVquNyqoZ49e8pbU8RYNbmWlSvV+5SUGq2bQuSyUlMrLga1P/7+d3XfanI9uhqjJhKys4EjR9QJMVyWRdR6CQlm2f19+rQJe/dq3RpqLQY16bbbe8AAC/z8uF0fUWt5e4viJ+qH3a++0ro11FoMatJtUI8Ywatporbq1k0dPlq0CFD4udclMahJV0ThuMxM9d2kT58zUPjOQtQmYp6Hr6+CY8eAQ4e0bg21BoOadGXTJlFK1oDAQAuWLs2Vs/WJqPVESA8dql5Vc+tL18SgJl3O9k5LsyIoKErr5hC5hdGjGdSujEFNuiF6uS+tn+ayLCJnueOOGnh7q13fYkUFuRYGNenGoUN2nDoFuZlAQgInkhE5S0iIgjvuUI85+9v1MKhJN/71rwp5HxFRDItF3aaPiJzj/vvVjW3mz7fCzr0vXQqDmnTj22/VTe5TUqq1bgqR2xk06Jzc2/3wYW9s335p+1/SPwY16UJZGfDjj2qN9sTEMq2bQ+Q2FEXsS12A2tqzSExUh5SWLvXXull0DRjUpAvr1wN1dQaEhVkRHGzRujlEbsNsLsTUqWZMnXoeKSkl8rnFiwNY/MSFMKhJV8uyUlM5iYzI2QIDoxEYGIWUlEpZ+/vUKRN++EHrVlFLMahJc2Jey+rV6jGXZRG1b+3v9HT1d2zOHK1bQy3FoCbN7dplx7lzQECADbGxvKImak8ZGWq1v3nz7Kip4exvV8CgJs0tWqQuxYqIKEJNDUuGErWn8PBc+PvXorTUiPnzS7VuDrUAg5p0tCyL3d5E7c1oBNLT1Q/Eixdz9rcrYFCTZkTRhf37C7B3r7d8nJBQrnWTiDxCerr6u7Zhgx9K1IngpGMMatKMWNv5zDM1UBQDIiJqEBho1bpJRB4hIsKCyEgrLBaD3Kea9I1BTZrKz4+W94mJHJsm6kg9e1bJe87+1j8GNWmmrg44fdpXHjOoiTpW9+5mGI0Ktm4Fjh7l7G89Y1CTZnbt8kFtrRG+vnWIiuJEMqKOZDAUICZGHaB+/31+UNYzBjVpJjPTt34SmZiJSkQd6/rr1Q1wFi4MgJVTRHSLb4+k+bIsbsJBpA0x5CQKDZ0/74UVK7RuDf0SBjVpIicHyMryltvuxcdzWRaRFkRP1nXXqd3ef/97Dfep1ikGNWli1Sr1vksXC/z8bFo3h8hjpabm1g9F7d7Nfar1iEFNmgZ1SgprexNpKSSkFrGxYqmWAf/6FyuV6RGDmjpcTY0Yn1aPU1M525tIaz16qPNE5s8PgI0dXLrDoKYOt3kzUFUFxMTYEBlZp3VziDxecnIl/PzsyM83Yd06rVtDl2NQU4dbuVK9HzGiBgaD1q0hIpNJQUaGWqls9mytW0OXY1BTh1IUEdSKPO7XrwCKeIKINNerlxrUy5ZB7g9P+sGgpg517Bhw8qQBRqMdO3dmw2xmRSQivWzU0atXpSzt+9lnXKalJwxq6jBijea8eeqklfh4C0JDw7VuEhFdZDYXIjAwXx7/4x922ftF+sCgpg7d1vLjj9Ufubg4Fjkh0puMDAO8ve04dcokJ32SPjCoqcOUlBhQUBAkj5OSKrVuDhFdxttbQY8e6lj1jBlVrFSmEwxq6jCZmX5QFAPCw6sRHMxlWUR6lJZ2Rt4vW+aHrCzOKtMDBjV1mLVr1U04kpJKtW4KEf2CqKgqhIfXwmYz4ptvWKlMDxjU1CFqa4FNm9RtLZOSuFsWkV6J2gbdu1+qVEbaY1BTh9i4UcwqNSIw0CY/sRORfqWnl8PLS8HBgz7Ys0fr1hCDmjrE0qXqfVoaq5ER6Z0oJ5qWVi2PZ8wwc1KZxhjU1O7E77ioduQIaiLSv65d8+T9vHm+yM4u0Lo5Ho1BTe3up5+A/HwgMNCOhARua0nkCuLiKhAUZIXFYsLq1ZxUpiUGNbW7b75RSxwNGlQmx72ISP84qUw/GNTU7r7+Wl0zXVWVy9reRC6ke3dRQVDB9u2+OHFC69Z4LgY1tavsbODIEW8YDArS03k1TeRKgoLqkJSkDlfNmFHBSWUaYVBTu/rqK/U+Ls4CPz+b1s0homuUnq5u1DF7ti/OnOGkMi0wqKldLVig3nfvri71ICLXIgoU+fraUFXlU1+0iDoWg5razbFjduzeDTmBrGtXBjWRKxK/v926qbvdLVjASWVaYFBTu/nkE3WHrM6dL0BRKrRuDhG1kmP29/r1fjjHfTo6HIOa2s2yZeray27duKUlkSsLD7cgJqYWdXUGzJxZxkllHYxBTe3i0CEgK8sbRqOC5GRuwkHk6tLT1Ylk77/vg7NnOamsIzGoyenEp+3Zs9Wu7uTkGjkRhYhcW1paCUwmO0pL/bF7t7fWzfEoDGpyOvFp+7PP1F/k5GTuPU3kDnx87EhNVT+Ac1JZx2JQk9MdOmRCWZkfvLzsSEzk+DSRe1UqE7vh+eH48bMcq+4gDGpyuqVL1UlkiYll8PFhNTIidxETU42QkBpUVXnhscdqUVDAseqOwKAmp1KUS7O9xZgWEbnbRh0X5PHx4120bo7HYFCTU333HXDmjEmOZ4kraiJyL+npxbJ2f36+L37+2Uvr5ngEBjU51eefq/fdulXDZGK3N5G7CQy0IiFB3QXvX//ipLKOwKAmp6msBBYuVI+vu44lQ4nclWNS2aJFAbBYtG6N+2NQk9MsXmyH2G46Pr4GXbqoW+MRkftJSqpEYKAN5897YckSrVvj/hjU5DQff2yV9+HhuaiqUrvGiMj9GI1Ar15V8njmTK1b4/4Y1OQUp04B27eLLfAUXH99jdbNIaJ21quXWe6sJSaQHjigdWvcG4OanOLLL9X7xEQLgoLUK2sicl9BQXW4/Xa18uDMmZw42p4Y1NRmdXV2fPJJnTzu2ZNd3kSewGwuBJArj+fMUVDG1ZjthkFNbbZsWTFyckwwmeoQG6sWQyAi95ea6ofwcCuqqoyYM0fr1rgvBjW12cKF6lrKrl1LuXaayMMqlfXpo/aizZqlViYkDwjqpUuXIjU1FSaTCX379sWRI0fk8wcPHsTAgQMRFhaG559/HkqDn4jNmzcjIyMDkZGRmDp1aqOvt3jxYiQlJSE2Nhbz58/v8O/HnYmC/KIw/7JlvheLnPBqmsjTZGSY4e9vg3ir3rRJ69a4J10F9YkTJ/Doo4/izTffRF5eHrp164YJEyagtrYWY8aMQf/+/bFr1y4cPnwYn18sgXX+/HmMHTsW48aNw44dOzB37lxs3LixPtzHjx+PyZMnY+3atXjxxRdx9OhRjb9L9yEK8j/+eC2qq70QHGxBdDTHp4k8TV3dOcTEnJbHM2Zo3Rr3pKugFlfPIqR/+9vfIjo6Gn/4wx+wZ88erF69GmVlZfJqOS0tDW+88QY++eQT+XdEMIurZRHG6enpMowd52bPno1hw4bJsO/VqxcmTpyIORxIcaqff1YL83frVi67wYjI82RknJf3S5cqOHaMW1+6dVDffffdePzxx+sfi6tfEb779u3DoEGDEBCgjoX27t1bXlUL4pwIY8PFlLjxxhuxe/fu+nPDhw+v/3oNzzVFXLmXl5c3utEvy8nxwpkz6tppEdRE5Jl8fM4gNrYYimLAlCksH+zWQd2QxWLBu+++iyeeeEIGZkpKSv05EcpeXl4oKSm54lxwcDDy8/PlcXPnmjJlyhSEhITU3xISEtrt+3MHixer21nGxVXINZVE5Ln69XNs1OGP4mKtW+NedBvUL730EgIDA2W3tZhY5uurTlhy8PPzQ1VV1RXnHM8LzZ1ryqRJk2QXu+OWm6uuEaQr2WzAggVqDwcnkRFRbGw1oqKsqK424sMPtW6Ne9FlUGdmZmLmzJmYN28evL29ER4eLieNNVRRUQEfH58rzjmeF5o71xQR6uKqu+GNmrZhA5CXJz4I2ZGSolYnIiLPJUYf+/evkMfvvWdDdTXHqt02qLOzs+UMbhHUPXv2lM+JZVliRnfDPyPGk0UQX35OTD6Li4tr8u81PEdt89FH6vK4jAzRq8HFk0Qkds47BX//WhQWeuHDD1mqzC2Durq6Wk4ou+eee3DfffehsrJS3oYMGSLHmz/77DP558Ss75EjR8pxarE0a9u2bdiwYQOsViveeust3HnnnfLPPfDAA1iwYAEOHDggv86MGTPqz1HrnTsnqpGpx127FmndHCLS1a5a6sTSjz4KYgEUJzFBR9atWydnc4vbxx9/3OgKWiy1ElfaotiJ0WjEposr60WRk2nTpmHUqFEICgpCaGho/RrrPn364JlnnsGAAQPk+LSYQf7kk09q9v250wYcdXUGRESUIjycu8YT0SUZGWXYsycCWVneWLcO4LWRmwW1uJJuWHGsoeTkZFkQRSyvEku1IiIi6s+JmeHiSjkrK0tefYvAdnj99ddl0RNRQGXo0KHNjlHT1Yn/ntmz1eOuXcVku8aT/IjIs4l5K2Kv6p9+CsK77zKo3S6oryYmJgajR49u8pxYhtVwKVZDYqzbMd5NbbNlC3DsGBAYaEdi4lnxEUrrJhGRzvTtW4G9ewOxfr0Be/fa0bevrkZZXQ5fPbomjqvpe++thre3TevmEJEOmUxnER+vzl95440arZvj8hjU1GIlJaLIiTo0ceedub84TEFEdMMNas2Kb77xR16e1q1xbQxqarEvvgBqagwID6/CmjWn5Sx9IqKmdO5ci7i4WlitBly2qSFdIwY1tYjdDvz97+rxDTdYEBQUpXWTiEjnbrqpUt6LSmVFXMnZagxqapGVK+04cQLo1KkOPXr8chlWIiKHpKRa9O5tgajcPH261q1xXQxqapGpU9X10vHxubBY1E/JRETNs+M//kPdM+H99xWUstpwqzCo6aqOHgU2bfKT21n26aPukENEdDVmcyF++ikfYWHVKC831A+f0bVhUNNVOX650tJqEBzMSmRE1HJBQdG4+WZ1iZbo/q5kh9w1Y1BTs8rLgYsVWdG3L6+miejadetWjeTkOly4APzjH1q3xvUwqKlZn31ml5+AU1KqkZBQq3VziMgFGQxirFpdTP3OO4qcXEYtx6CmZpdkvfeeuqdsZGQ2qqp4RU1ErRurPnYsF0FBtSgoMOC118phF28w1CIMavpFYivL7GyTLLJ//fUcmyai1gsO7oyBA8X+AMC77wYgK+uc1k1yGQxqapLNZsff/qaGc+/eZnh789MvEbVNWloxwsNrYbGYMHPmpV0OqXkMamrSsmXF+OknHxiNNnTvXqh1c4jIDRiNwI03qiXKPv00iDXAW4hBTU1yfNrt1q0YAQHcJYuInCMhwYzY2Fq5b8ALL5g5Vt0CDGq6wsGDwLffOgqc8GqaiJzHYAAGDDglj+fNC8DWree1bpLuMajpCm+9pd6np9cgJIRLsojIuWJizEhMrISiGPDmm8FaN0f3GNTUSE4OMH++ejxwIEsIEVH7EGPVBoOC1av9sX691q3RNwY1NTJtGlBXBwweXIuYGKvWzSEiNxUebkG/fmpthqeeAixcAfqLGNRUr7gY+PhjRR6PG5cDRVGPiYjaw803VyAy0iY3/nnvPa1bo18Maqo3c6aoIGRAREQVtm8/CbOZlciIqP34+NgwcaK6DearrypcrvULGNQkVVbaMX26ugzrxhtrEBQUpXWTiMhDSotGR1eistKA55/XukX6xKAmacaMchQXeyEoqApxcWpBAiKijtgGc+RIs5xYJiayZmZyXfXlGNQkJ3HMmqUWOOnTp0hWDyIi6iiBgXno2lWtA/7ooxacOJHPQigN8C2ZMGcOkJdnQmCgKBd6QevmEJEHGjy4CgEBFuTk+OHBB40oKCjQukm6waD2cBaL2HyjTh73718Jk4kzvYmo4/n42HHrrerEsn37onHggLfWTdINBrWH+/DDMpw+LbaytCAtjeVCiUg7ycllSE2tkBXLnnsuRNZ0IAa1R7PZxNrFTvK4T5/z8Pbm1TQRaeuWWwrh62vHwYM+ePddrVujDwxqD7ZoEXDypAl+fnb07MnC+ESkPbFb39ChpfL45ZcVZGVxUhmD2kOJCZWvvaYe33BDpRwfIiLSg6SkU+jSpURuhfnoo1b5fuXJGNQe6quv7Dh0SCyLsKFPH26+QUT62grz9ttL4O1tx/ff++Ltt0s9erkWg9oDiRLer7yiViFLSTkFm41BTUT60qlTHQYMUGuKTp7cCbt3e+7wHIPaA61YARw65C0/rYpubyIiPbruuvOIjq6G1eqFP/851GO7wBnUHsZms+PFF9X95Pr0McPPT72yJiLSG1El8fbbC2Ay2bF1qy8++AAeiUHtYebOLcHevT7w8rIhI4PrpolI30JCrBgypEweP/ecHUePet5lNYPag4huozffDJbH111XBH9/Xk0Tkf517ZqN6OhS1NQYMX58nawB4UkY1B5k3jzgyBFvWUygb1/W0SUi15kFPmJEsVxGunu3D158sdyjZoEzqD1EdbUd//M/aj2+AQMqODZNRC4lKKgON9+cI4+nTOmENWs8ZwMhBrWHePfdCuTmmuDvX4Pu3Tk2TUSup1u3YqSkqLXA//jHMFRUwCMwqD2A+GGePl3db7p//0LukEVELtsFfttt5+Qa61OnTJg4ER6BQe0Bpk4FLlzwQmhoHXr0KNK6OURErebra8eoUaUwGhV8+SXwz3/C7TGo3Vxenh1vv61OurjllnK5LpGIyJXFxtZgwoR8efzEE3YcP+7eE8v4tu3mnn66BmazERERpYiL49U0Ebk+s7kQp0+vRlRUmXx/++1v62BR6zi5JQa1G/vuO+DrrwNEdW/cdluBHN8hInIH/v5huOOOC7IrXBRx+vOfK9x2yRaD2k1ZraifaNGrVxWioqq0bhIRkdOXbN122yl5PHNmJyxcWAJ3xKB2U2Jcev9+UX6vDoMHl2vdHCKidpGSUoqMjFJ5/MwzoSh0w9WnDGo3dOwY8Oqraj/3ddcdht3uIYsNicgj3XzzeYSHW1FY6IVx42rk5kPuhEHtZkQN3P/8T6C21oCkpBpcd51ajYyIyF2ZTAqGDz8Oo9GOzEw/vP66e12cMKjdsMt761YgMNCGESNKOYGMiDxCeHiNvLIWXn89GHv2wG0wqN2EmO24bl0hJk9WH/fqdRgmE8emichz9OxZhrS0algsBjz0kFqV0R0wqN3EqVMFeOihTqirMyI5uQK9elm1bhIRUYcyGIA77ihB584WOVfnsccUKG5QMZlB7SbEPtMlJf7w9xebrBeyy5uIPJLdXoA+fY7AYFDwr38ZMHMmXB6D2g2sW2fHxx+rm24MHXoa/v7cwpKIPFdKij9uu00d+vvTn4CNG+HSGNQuLi8PGD9e7dsRawkTEzkuTUR0ww1m3HtvFerqgPvvB44ehctiULt49TExYaKoSOyMVV4/45GIiOz4f//vKPr3t6C0FLjrLuDMGbgkBrUL+5//gVyK1amTHUOG7OE+00REDTbumDmzEhkZh5GQUINTp4ARIxScOweXw6B2UUuWAO+8ox6/+OIpBAWZtW4SEZGuBAZGIyDAjv799yMoqBbHjhkwdChw+jRcCoPaBR0+bMcjj6gl8nr1Ooe9e4+jurpa62YREelS586h+M1vihETUyvHqm+6yYbMzEKX2W2LQe1iRMH5X//ajvJyI6KiSnDTTWUIDIzSullERLrm7X0WN9+8FyEhFTh3zgu//nU4li0rhitgULuQykpg7FggJ8ckd8W6665cGPk/SETUIlFRYbjnnpOIjhbVy0wYNy4CixdD9/g27yJEz7YI6R9+AEJD7bjvvgvw9+eGG0RE18LPz4bRo88gJaUaNTUG/OY3Yp6PAj33gjOoXeRK+p571EX7QUF2TJ9+DGFhDGkiotYQK2RGjDiCHj3U9Vp/+5sBY8ZUo7xcn2nNoHaBMelhwxSsX69+Ehw27BjWrz8Ns5mzvImIWksMG952WxWGDj0lt8dctcofgwbZZI1wvWFQ69j27UD//sCuXQb4+tZh6NAf0aWLkZPHiIicpHv3YowZcwYBATYcOeKNfv3s+PJLfV1ZM6h1qKoKcrvK225TZCWdxMQajBt3AUlJPlo3jYjI7URH1+C++w4jOroUVVVGPPywEQ89VIWKCn0ENoNaZyVBv/wSuO46Ba+9BthsBqSlFWPQoK3w8SnTunlERG4rMNCKMWMK0b9//sWdtwLQp49dVn/UmkcE9cGDBzFw4ECEhYXh+eefh6KzDUrFVfPrrwPp6QoefljsLW1AYKAFQ4bsxfDhRQgNDde6iUREHjFu3b9/gZwVHhhoQ3a2SfZs/v73Cs5ruJWC2wd1bW0txowZg/79+2PXrl04fPgwPv/8c62bJavjvPmmKG1nQUIC8Ne/irJ2BrmfdN++x/Db355GRoad+0oTEXWw2NhqPPjgQaSlFUBRDPjoIwO6drXj1VftKCnp6NYAJri51atXo6ysDFOnTkVAQADeeOMN/PGPf8Sjjz7aYW0Q6/MOHQK2bXPcFGRnGy5+TlLHnaOiytC1aw4yMvxgMlUCUPeXJiKijufra8OIEeXo2bMcW7fGoqQkCC+9BLz9toKXXjLguec6ri1uH9T79u3DoEGDZEgLvXv3llfVHenmmxXs3Nnw0tgglwNERxcjLc2GpKRCBAb6ilFq8ePRoW0jIqJf1qVLJR54IB8nT3pj164wlJeH4mKcdBi3D+ry8nKkpKTUPzYYDPDy8kJJSYkcs768m1zcHMSVuONrtEViohm7d/siNLQIYWFlCAs7i/h4f3h7VwAIgNVahdJS8T9fJR+35r6yMg8+PoFt+hr8Nz3z33T374//Jv9NOOE+MrIKQ4aYcc89QzB2bDjaGAv1OnXqJHPJo4PaZDLB17fxVaqfnx+qqqquCOopU6bglVdeueJrJIhBZCe4cEG9CT/+6JQvSUREHWjlSuDxx5339cQFYXBwsGcHdXh4uJz13VBFRQV8fK5ckzxp0iT8+c9/rn8stkArLi5GRETEVT/xNEdckYuwz83Nvep/iCfj69RyfK1ahq9Ty/G10uZ1ElfUV+P2QS2WZX388cf1j7Ozs2X3tgjwy4kr78uvvkNDQ53WFvGfyl+Aq+Pr1HJ8rVqGr1PL8bXS3+vk9suzbrvtNvkJ6LPPPpOPxazvkSNHynFqIiIivfOIMerZs2dj3LhxstiJ0WjEpk2btG4WERFRi7h9UAtjx47FiRMnsHv3brlUS4w5dyTRnf7SSy9d0a1OjfF1ajm+Vi3D16nl+Frp93UyKHqrp0lERESeM0ZNRETkyhjUREREOsagJiIi0jEGtYdvsamlpUuXIjU1Vc7M79u3L44cOSKf52v2y+6666763d82b96MjIwMREZGyk1n6JIXXnhB7prnwJ+pxsRKGFG0Q+yBcPvtt+PkyZPyeb5OqqKiIll6+tSpUxefaf61ae/fRQa1B26xqQdiFr7YwezNN99EXl4eunXrhgkTJvA1a8bcuXOxdu1aeXz+/Hm5mkEsO9yxY4c8t3HjRq2bqAv79+/HrFmz8N5778nH/Jm68nfv1VdflR+Us7KykJaWhkceeYSvU4OQvvvuuxuFdHOvTYf8LopZ39Q+lixZooSFhSlms1k+3rt3rzJ48GCtm6ULy5cvV/7xj3/UP87MzFT8/f35mv2CCxcuKNHR0Ur37t2Vzz77TJk2bZrSo0cPxW63y/PffPONMn78eMXT2Ww25aabblImT55c/xx/phpbtGiR8pvf/Kb+8datW5UuXbrwdbpoxIgRynvvvScul5Xs7Gz5XHOvTUf8LvKK2s232NQr8Yn18QaV7Y8ePYr09HS+Zr/g2WefxX333SdfG0G8TsOGDauvQX/jjTfKOgGe7sMPP8SBAweQnJyMZcuWwWKx8GfqMj179kRmZib27t0rN4QQvQ933HEHX6eLRMnpp59+Gg0199p0xO8ig1qjLTbpEvFm+u677+KJJ57ga9YE0Y327bff4q233qp/7vLXSdQczs/PhyerrKyUhSjEvIfTp09j2rRpuPXWW/kz1URQP/jgg+jXr5/cy0B0177zzjt8nS5q+Bo4NPfadMTvIoNaoy026RLx5hoYGCjHqPmaNVZTU4Pf//73+OCDDxrtsnP56+TJr5HD119/DbPZLD/YiO1q169fL3fK+/TTT/kz1cDOnTuxfPlyfP/99ygtLZVjq6NGjeLvXjOae2064neRQd2OxA5dYqJBS7bY9FSiC27mzJmYN28evL29+Zpd5m9/+5ucaTp69OhGz1/+Onnya+Rw5swZ2T0pZt4K4g1UdFGKMOLP1CXz58/HQw89hJtuugkhISF47bXX5AQz/u79suZem474XfSIWt+usMWmJxKvh/g0L4JadMcJfM0aEx9gxJuAY7tV8Ul94cKF8viWW26p/3N79uxBXFwcPFl8fDyqq6sbPSe6wKdPn47333+//jlP/5my2+1yZnPDYHFcGYpucAdPf50aau59SZwTv6ft+rvo1Klp1IjValWioqKUTz/9VD6eMGGCcvfdd2vdLF2oqqpSevbsqTz22GNKRUVF/c1isfA1ayA3N1fOPHXcHnjgAeXtt99Wzp8/r/j5+Snr16+Xr9ldd92lTJw4UfFkRUVFSnBwsPLBBx/I103M3BWvUU5ODn+mLpv1HRAQoEydOlWZO3euMmzYMCUpKYm/e5dpOOu7uffyjvhdZFC3s6VLl8pfioiICPkffejQIa2bpAtiCYP4Rbj8Jn4x+Jr9socfflguzxJEIHl7e8tlIykpKUpBQYHi6cRSo0GDBsmlfqmpqcqyZcvk8/yZukQsI3r11VeVxMRE+fPTr18/5aeffpLn+Do1HdRXe23a+3eRu2d1gIKCAs222HRVfM1aRnTBiaIVQ4YMQVBQkNbN0TX+TLUMX6fWvTbt+bvIoCYiItIxzvomIiLSMQY1ERGRjjGoiYiIdIxBTUREpGMMaiIiIh1jUBNRm23atEnuWNUa//Zv/4annnrK6W0ichdcnkVEbVZXVyfLUIqdg67FqlWr8Oijj8ptTh1lUomoMV5RE1GbiTrR1xrSYqerJ598ElOmTGFIEzWDQU1E9R555BG8/PLL+Oc//4nu3bvL7TWFDRs2yI1TAgIC5GYgP//8c5u7vsVWlGIvchHyYktKsVkEEV2JQU1EjaxduxZ///vf8c4772Ds2LHyud/97nf1XdQisCdPntymf0PsavXee+8hJSUFJ0+exAsvvIB7772XYU3UBG5zSUSNiL2Jjx8/LvcqdvD394fVapXb+n300UdyTLotvvjiC0RHR+Pbb7+Fn58fnn32WSQlJckr91/96ldO+C6I3AevqImokYcffrhRSAuiK3zjxo1yn91hw4bh4MGDbfo3zpw5g5EjR8qQFjp16oT09PQrutSJiEFNRJcJDAxs9FjM5hZX0GIcuaioCLfeeqscy26L+Ph4VFdX1z8WXd4ivMUHASJqjEFNRM0SIX3nnXdi7ty5KCwsFHvYt7nr+ze/+Q2WLVuGr776Sgb0pEmTZNe6uMomosY4Rk1EzRLLrkTXt5hANmHCBHTt2hUffvhhm75mRkYG5s+fL7/msWPH5NdcunTpFVfzRMSCJ0RERLrGrm8icrqcnBxZxKSp20MPPaR184hcCq+oicjpxBi2GHtuiiia0rlz5w5vE5GrYlATERHpGLu+iYiIdIxBTUREpGMMaiIiIh1jUBMREekYg5qIiEjHGNREREQ6xqAmIiLSMQY1ERER9Ov/A3118HDuJnblAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.displot(tezheng,x='rsi_6',kde=True,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.26% (0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor='股息率(TTM)(%)'\n",
    "target='return_s1'\n",
    "\n",
    "j=sns.jointplot(x=factor, y=target, data=tezheng,kind='scatter')\n",
    "r, p = spearmanr(tezheng[factor], tezheng[target])\n",
    "print(f'{r:,.2%} ({p:.2%})')#R为相关系数 P为对应的P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征矩阵的多因子相关性分析\n",
    "\n",
    "* sns.clustermap\n",
    "\n",
    "`sns.clustermap` 是 Seaborn 库中的一个高级函数，用于生成带有层次聚类的热力图（Hierarchical Clustering Heatmap）。它的主要目的是对数据矩阵（如相关系数矩阵、表达数据等）进行可视化，并通过行和列的聚类来揭示数据内部的结构和模式。\n",
    "\n",
    "`sns.clustermap` 的意义和特点包括：\n",
    "\n",
    "1. **数据结构发现**：通过对数据集的行和列应用层次聚类算法（如平均链接法、单连接法、完全连接法等），`clustermap` 可以发现数据中的隐藏群体或模块，这对于探索复杂数据集中的自然分组尤为有用。\n",
    "\n",
    "2. **热力图可视化**：以颜色编码的方式展示数据值，颜色的深浅代表数值的高低，直观地展示数据矩阵中的数值分布和强度。这对于观察变量间相对大小和模式非常有帮助。\n",
    "\n",
    "3. **聚类指示**：通过 dendrogram（树状图）展示行和列的聚类关系，用户可以直接从图中读取哪些行（或列）被聚类在一起，从而理解数据的分组依据。\n",
    "\n",
    "4. **交互式探索**：生成的热力图通常具有交互性（取决于使用的环境，如 Jupyter Notebook），用户可以放大、缩小或悬停查看具体的数值，便于详细分析。\n",
    "\n",
    "5. **高级定制**：提供了丰富的参数选项，允许用户自定义颜色方案、聚类方法、距离度量、注释、图例等，以满足特定分析需求。\n",
    "\n",
    "总的来说，`sns.clustermap` 是一个强大的数据分析和可视化工具，尤其适用于生物学（如基因表达分析）、社会科学、市场研究等领域中，需要从大规模数据集中寻找模式、相关性和分类的场景。通过同时展示数据的数值强度和结构关系，它极大地增强了数据探索和解释的能力。\n",
    "\n",
    "* sns.PairGrid\n",
    "\n",
    "`sns.PairGrid` 是 Seaborn 库中的一个灵活的图形接口，用于创建成对关系的子图网格，主要用于绘制数据集中所有变量两两之间的关系图。这对理解多维数据集中的变量间相互作用非常有帮助。其意义和功能主要包括：\n",
    "\n",
    "1. **灵活的布局**：`PairGrid` 会为数据集中的每一对变量创建一个子图，你可以自定义这些子图中显示的内容，比如在非对角线位置上绘制散点图表示两个变量之间的关系，在对角线位置上显示直方图或核密度估计图来展示单个变量的分布。\n",
    "\n",
    "2. **自定义绘图类型**：通过 `map` 或 `map_upper`, `map_lower`, `map_diag` 方法，用户可以指定在每个子图上绘制何种类型的图表，比如散点图(scatterplot)、线性回归图(regplot)、核密度图(kdeplot)等，这使得 `PairGrid` 极其灵活，能够适应多种数据分析需求。\n",
    "\n",
    "3. **分组和着色**：通过 `hue` 参数，可以在图表中根据另一个变量对数据点进行分组和着色，这对于探索第三个变量如何影响对变量间的关系尤为重要，增加了可视化分析的维度。\n",
    "\n",
    "4. **高级控制**：`PairGrid` 提供了对每个子图的精细控制，允许用户调整图表的各个方面，如颜色、标记样式、标签等，以更好地讲述数据故事。\n",
    "\n",
    "简而言之，`sns.PairGrid` 的核心意义在于提供了一种机制，用于系统性地探索数据集中所有变量之间的成对关系，同时保留了高度的可定制性，使数据科学家和分析师能够深入挖掘数据中的模式和关联，特别是在探索性数据分析阶段。\n",
    "\n",
    "* sns.jointplot\n",
    "\n",
    "`sns.jointplot` 是 Seaborn 库中的一个功能强大的函数，用于创建联合分布图（Joint Distribution Plot）。这种图表主要用于可视化两个连续变量之间的关系，同时提供它们各自分布的概览。Jointplot 结合了散点图（展示两个变量之间的成对关系）和单变量分布的可视化（通常是直方图或核密度估计图，KDE），从而在一个图表中提供了两种变量联合及边际分布的完整视图。\n",
    "\n",
    "`sns.jointplot` 的意义和特点包括：\n",
    "\n",
    "1. **双变量关系展示**：通过散点图（默认类型）展示两个连续变量之间的相互关系，如相关性、聚集模式等。\n",
    "\n",
    "2. **边际分布**：除了中心的散点图，图表两侧或顶部底部还会显示每个变量的分布，通常使用直方图或核密度估计图（KDE plot），帮助理解每个变量的单独分布特性。\n",
    "\n",
    "3. **多种可视化选项**：`jointplot` 允许用户选择不同的图形类型（如散点图 scatter、回归线 reg、残差图 resid、核密度图 kde、六边形图 hex）以及统计函数来进一步分析变量间的关系。\n",
    "\n",
    "4. **探索性分析**：非常适合探索性数据分析，因为它快速提供了关于两个变量如何一起变化以及它们各自的分布情况的信息，有助于假设检验和发现潜在的相关性。\n",
    "\n",
    "5. **灵活定制**：用户可以根据需要调整颜色、大小、比例等图表属性，以及添加额外的统计拟合线，以更好地解释或描述数据特性。\n",
    "\n",
    "总之，`sns.jointplot` 提供了一种强大的方式来综合分析两个连续变量之间的复杂关系，并且通过边际分布的展示，使得对这两个变量的单独理解以及它们之间的联合分布有了更加全面的视角，这对于统计分析和数据探索而言是非常有价值的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_corr=tezheng.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>成交额(千元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>量比</th>\n",
       "      <th>市盈率(静态)</th>\n",
       "      <th>市盈率(TTM)</th>\n",
       "      <th>...</th>\n",
       "      <th>rsi_6</th>\n",
       "      <th>rsi_24</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "      <th>return_30</th>\n",
       "      <th>return_5</th>\n",
       "      <th>return_10</th>\n",
       "      <th>close-o</th>\n",
       "      <th>high-l</th>\n",
       "      <th>return_s1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>return_s1</th>\n",
       "      <td>-0.002781</td>\n",
       "      <td>-0.002701</td>\n",
       "      <td>-0.002671</td>\n",
       "      <td>-0.002690</td>\n",
       "      <td>-0.010789</td>\n",
       "      <td>-0.011780</td>\n",
       "      <td>-0.030243</td>\n",
       "      <td>-0.014275</td>\n",
       "      <td>-0.003174</td>\n",
       "      <td>-0.000616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012287</td>\n",
       "      <td>-0.001576</td>\n",
       "      <td>-0.002655</td>\n",
       "      <td>-0.002691</td>\n",
       "      <td>-0.007671</td>\n",
       "      <td>0.008091</td>\n",
       "      <td>0.003990</td>\n",
       "      <td>0.003882</td>\n",
       "      <td>-0.003159</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsi_6</th>\n",
       "      <td>0.016514</td>\n",
       "      <td>0.018744</td>\n",
       "      <td>0.017765</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.132404</td>\n",
       "      <td>0.145176</td>\n",
       "      <td>0.254396</td>\n",
       "      <td>0.224125</td>\n",
       "      <td>0.041311</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.768101</td>\n",
       "      <td>0.005206</td>\n",
       "      <td>0.004033</td>\n",
       "      <td>0.491441</td>\n",
       "      <td>0.775536</td>\n",
       "      <td>0.765123</td>\n",
       "      <td>0.135416</td>\n",
       "      <td>0.042053</td>\n",
       "      <td>0.012287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_5</th>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.142120</td>\n",
       "      <td>0.152843</td>\n",
       "      <td>0.291029</td>\n",
       "      <td>0.234986</td>\n",
       "      <td>0.041162</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775536</td>\n",
       "      <td>0.566387</td>\n",
       "      <td>-0.000401</td>\n",
       "      <td>-0.002035</td>\n",
       "      <td>0.394584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700945</td>\n",
       "      <td>0.114734</td>\n",
       "      <td>0.032832</td>\n",
       "      <td>0.008091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股息率(%)</th>\n",
       "      <td>0.025713</td>\n",
       "      <td>0.025004</td>\n",
       "      <td>0.026291</td>\n",
       "      <td>0.025584</td>\n",
       "      <td>-0.015134</td>\n",
       "      <td>-0.084152</td>\n",
       "      <td>-0.137976</td>\n",
       "      <td>-0.000943</td>\n",
       "      <td>-0.203916</td>\n",
       "      <td>-0.012941</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032199</td>\n",
       "      <td>-0.066985</td>\n",
       "      <td>0.024366</td>\n",
       "      <td>0.029261</td>\n",
       "      <td>-0.063777</td>\n",
       "      <td>-0.029812</td>\n",
       "      <td>-0.041034</td>\n",
       "      <td>-0.005181</td>\n",
       "      <td>-0.011977</td>\n",
       "      <td>0.006233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_10</th>\n",
       "      <td>0.013507</td>\n",
       "      <td>0.015153</td>\n",
       "      <td>0.013945</td>\n",
       "      <td>0.015370</td>\n",
       "      <td>0.169580</td>\n",
       "      <td>0.189296</td>\n",
       "      <td>0.350873</td>\n",
       "      <td>0.140443</td>\n",
       "      <td>0.057836</td>\n",
       "      <td>0.000842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.765123</td>\n",
       "      <td>0.703014</td>\n",
       "      <td>0.002090</td>\n",
       "      <td>-0.000972</td>\n",
       "      <td>0.567529</td>\n",
       "      <td>0.700945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080467</td>\n",
       "      <td>0.044950</td>\n",
       "      <td>0.003990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close-o</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.015036</td>\n",
       "      <td>0.012924</td>\n",
       "      <td>0.024808</td>\n",
       "      <td>0.020167</td>\n",
       "      <td>0.052441</td>\n",
       "      <td>0.019925</td>\n",
       "      <td>0.034984</td>\n",
       "      <td>0.006326</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135416</td>\n",
       "      <td>0.078153</td>\n",
       "      <td>0.007611</td>\n",
       "      <td>0.005140</td>\n",
       "      <td>0.050211</td>\n",
       "      <td>0.114734</td>\n",
       "      <td>0.080467</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068582</td>\n",
       "      <td>0.003882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股息率(TTM)(%)</th>\n",
       "      <td>0.024797</td>\n",
       "      <td>0.024083</td>\n",
       "      <td>0.025357</td>\n",
       "      <td>0.024658</td>\n",
       "      <td>-0.012740</td>\n",
       "      <td>-0.079034</td>\n",
       "      <td>-0.135914</td>\n",
       "      <td>-0.003163</td>\n",
       "      <td>-0.214485</td>\n",
       "      <td>-0.015713</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043280</td>\n",
       "      <td>-0.082046</td>\n",
       "      <td>0.023466</td>\n",
       "      <td>0.028307</td>\n",
       "      <td>-0.074283</td>\n",
       "      <td>-0.036254</td>\n",
       "      <td>-0.048661</td>\n",
       "      <td>-0.005688</td>\n",
       "      <td>-0.012458</td>\n",
       "      <td>0.003668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市盈率(TTM)</th>\n",
       "      <td>-0.003790</td>\n",
       "      <td>-0.003778</td>\n",
       "      <td>-0.003795</td>\n",
       "      <td>-0.003784</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>0.011780</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.068595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>-0.003782</td>\n",
       "      <td>-0.003846</td>\n",
       "      <td>-0.001574</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>0.000842</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>-0.002832</td>\n",
       "      <td>-0.000616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市盈率(动态)</th>\n",
       "      <td>-0.006395</td>\n",
       "      <td>-0.006164</td>\n",
       "      <td>-0.006569</td>\n",
       "      <td>-0.006343</td>\n",
       "      <td>-0.004149</td>\n",
       "      <td>0.022648</td>\n",
       "      <td>0.050468</td>\n",
       "      <td>0.002822</td>\n",
       "      <td>0.199799</td>\n",
       "      <td>0.013479</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018051</td>\n",
       "      <td>0.033446</td>\n",
       "      <td>-0.006166</td>\n",
       "      <td>-0.007625</td>\n",
       "      <td>0.043888</td>\n",
       "      <td>0.021153</td>\n",
       "      <td>0.030090</td>\n",
       "      <td>0.002159</td>\n",
       "      <td>0.005257</td>\n",
       "      <td>-0.000868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dong10</th>\n",
       "      <td>0.056435</td>\n",
       "      <td>0.060502</td>\n",
       "      <td>0.059289</td>\n",
       "      <td>0.062556</td>\n",
       "      <td>0.038306</td>\n",
       "      <td>0.097396</td>\n",
       "      <td>0.050601</td>\n",
       "      <td>0.019326</td>\n",
       "      <td>0.017272</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241366</td>\n",
       "      <td>0.219087</td>\n",
       "      <td>0.012619</td>\n",
       "      <td>0.011342</td>\n",
       "      <td>0.169959</td>\n",
       "      <td>0.202761</td>\n",
       "      <td>0.289318</td>\n",
       "      <td>0.264635</td>\n",
       "      <td>0.084648</td>\n",
       "      <td>-0.001090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsi_24</th>\n",
       "      <td>0.042869</td>\n",
       "      <td>0.044697</td>\n",
       "      <td>0.043051</td>\n",
       "      <td>0.044669</td>\n",
       "      <td>0.186115</td>\n",
       "      <td>0.228716</td>\n",
       "      <td>0.354580</td>\n",
       "      <td>0.112938</td>\n",
       "      <td>0.087843</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032079</td>\n",
       "      <td>0.027955</td>\n",
       "      <td>0.799439</td>\n",
       "      <td>0.566387</td>\n",
       "      <td>0.703014</td>\n",
       "      <td>0.078153</td>\n",
       "      <td>0.082214</td>\n",
       "      <td>-0.001576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACD_z</th>\n",
       "      <td>0.003197</td>\n",
       "      <td>0.005487</td>\n",
       "      <td>0.005313</td>\n",
       "      <td>0.006759</td>\n",
       "      <td>0.026259</td>\n",
       "      <td>0.043987</td>\n",
       "      <td>0.032702</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218099</td>\n",
       "      <td>0.139088</td>\n",
       "      <td>-0.040622</td>\n",
       "      <td>-0.038130</td>\n",
       "      <td>0.085826</td>\n",
       "      <td>0.201221</td>\n",
       "      <td>0.249019</td>\n",
       "      <td>0.153552</td>\n",
       "      <td>0.009358</td>\n",
       "      <td>-0.001915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_high</th>\n",
       "      <td>0.997486</td>\n",
       "      <td>0.997522</td>\n",
       "      <td>0.997255</td>\n",
       "      <td>0.997311</td>\n",
       "      <td>0.069426</td>\n",
       "      <td>0.432747</td>\n",
       "      <td>-0.041526</td>\n",
       "      <td>-0.007848</td>\n",
       "      <td>0.004438</td>\n",
       "      <td>-0.003782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003925</td>\n",
       "      <td>0.031151</td>\n",
       "      <td>0.999837</td>\n",
       "      <td>0.996886</td>\n",
       "      <td>0.018881</td>\n",
       "      <td>-0.002157</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.005673</td>\n",
       "      <td>0.876977</td>\n",
       "      <td>-0.002563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA20_close</th>\n",
       "      <td>0.998256</td>\n",
       "      <td>0.998108</td>\n",
       "      <td>0.998190</td>\n",
       "      <td>0.998055</td>\n",
       "      <td>0.067122</td>\n",
       "      <td>0.424657</td>\n",
       "      <td>-0.045227</td>\n",
       "      <td>-0.007531</td>\n",
       "      <td>0.002298</td>\n",
       "      <td>-0.003820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.028858</td>\n",
       "      <td>0.999242</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.015197</td>\n",
       "      <td>-0.003116</td>\n",
       "      <td>-0.001442</td>\n",
       "      <td>0.004521</td>\n",
       "      <td>0.868338</td>\n",
       "      <td>-0.002600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_low</th>\n",
       "      <td>0.996973</td>\n",
       "      <td>0.996610</td>\n",
       "      <td>0.997098</td>\n",
       "      <td>0.996741</td>\n",
       "      <td>0.064283</td>\n",
       "      <td>0.414273</td>\n",
       "      <td>-0.049454</td>\n",
       "      <td>-0.007144</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.003856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002385</td>\n",
       "      <td>0.026116</td>\n",
       "      <td>0.996359</td>\n",
       "      <td>0.999739</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>-0.004229</td>\n",
       "      <td>-0.003649</td>\n",
       "      <td>0.003166</td>\n",
       "      <td>0.856341</td>\n",
       "      <td>-0.002639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>up</th>\n",
       "      <td>0.997836</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>0.997635</td>\n",
       "      <td>0.997707</td>\n",
       "      <td>0.069726</td>\n",
       "      <td>0.433119</td>\n",
       "      <td>-0.041216</td>\n",
       "      <td>-0.007339</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>-0.003782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005206</td>\n",
       "      <td>0.032079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997371</td>\n",
       "      <td>0.019214</td>\n",
       "      <td>-0.000401</td>\n",
       "      <td>0.002090</td>\n",
       "      <td>0.007611</td>\n",
       "      <td>0.877082</td>\n",
       "      <td>-0.002655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.999878</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999872</td>\n",
       "      <td>0.068782</td>\n",
       "      <td>0.427705</td>\n",
       "      <td>-0.042639</td>\n",
       "      <td>-0.007179</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>-0.003795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017765</td>\n",
       "      <td>0.043051</td>\n",
       "      <td>0.997635</td>\n",
       "      <td>0.997871</td>\n",
       "      <td>0.026666</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.013945</td>\n",
       "      <td>0.012924</td>\n",
       "      <td>0.867567</td>\n",
       "      <td>-0.002671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>0.999731</td>\n",
       "      <td>0.999896</td>\n",
       "      <td>0.999872</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069728</td>\n",
       "      <td>0.430862</td>\n",
       "      <td>-0.041424</td>\n",
       "      <td>-0.005987</td>\n",
       "      <td>0.003616</td>\n",
       "      <td>-0.003784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.044669</td>\n",
       "      <td>0.997707</td>\n",
       "      <td>0.997574</td>\n",
       "      <td>0.028104</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.015370</td>\n",
       "      <td>0.024808</td>\n",
       "      <td>0.872661</td>\n",
       "      <td>-0.002690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>down</th>\n",
       "      <td>0.997760</td>\n",
       "      <td>0.997453</td>\n",
       "      <td>0.997871</td>\n",
       "      <td>0.997574</td>\n",
       "      <td>0.065252</td>\n",
       "      <td>0.417035</td>\n",
       "      <td>-0.048088</td>\n",
       "      <td>-0.007344</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>-0.003846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004033</td>\n",
       "      <td>0.027955</td>\n",
       "      <td>0.997371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.002035</td>\n",
       "      <td>-0.000972</td>\n",
       "      <td>0.005140</td>\n",
       "      <td>0.858936</td>\n",
       "      <td>-0.002691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.999853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>0.999896</td>\n",
       "      <td>0.070376</td>\n",
       "      <td>0.433146</td>\n",
       "      <td>-0.040521</td>\n",
       "      <td>-0.005488</td>\n",
       "      <td>0.004093</td>\n",
       "      <td>-0.003778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018744</td>\n",
       "      <td>0.044697</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>0.997453</td>\n",
       "      <td>0.028503</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.015153</td>\n",
       "      <td>0.015036</td>\n",
       "      <td>0.876535</td>\n",
       "      <td>-0.002701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999853</td>\n",
       "      <td>0.999878</td>\n",
       "      <td>0.999731</td>\n",
       "      <td>0.069282</td>\n",
       "      <td>0.429777</td>\n",
       "      <td>-0.041899</td>\n",
       "      <td>-0.006801</td>\n",
       "      <td>0.003471</td>\n",
       "      <td>-0.003790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016514</td>\n",
       "      <td>0.042869</td>\n",
       "      <td>0.997836</td>\n",
       "      <td>0.997760</td>\n",
       "      <td>0.026948</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.013507</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.871337</td>\n",
       "      <td>-0.002781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>流通股本(万股)</th>\n",
       "      <td>0.186648</td>\n",
       "      <td>0.186291</td>\n",
       "      <td>0.186977</td>\n",
       "      <td>0.186607</td>\n",
       "      <td>0.567982</td>\n",
       "      <td>0.332313</td>\n",
       "      <td>-0.117960</td>\n",
       "      <td>-0.007085</td>\n",
       "      <td>-0.078500</td>\n",
       "      <td>-0.007835</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011050</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>0.186033</td>\n",
       "      <td>0.188628</td>\n",
       "      <td>-0.012693</td>\n",
       "      <td>-0.006731</td>\n",
       "      <td>-0.009172</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.144462</td>\n",
       "      <td>-0.002911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high-l</th>\n",
       "      <td>0.871337</td>\n",
       "      <td>0.876535</td>\n",
       "      <td>0.867567</td>\n",
       "      <td>0.872661</td>\n",
       "      <td>0.103220</td>\n",
       "      <td>0.520673</td>\n",
       "      <td>0.020307</td>\n",
       "      <td>0.039641</td>\n",
       "      <td>0.032747</td>\n",
       "      <td>-0.002832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042053</td>\n",
       "      <td>0.082214</td>\n",
       "      <td>0.877082</td>\n",
       "      <td>0.858936</td>\n",
       "      <td>0.073110</td>\n",
       "      <td>0.032832</td>\n",
       "      <td>0.044950</td>\n",
       "      <td>0.068582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市盈率(静态)</th>\n",
       "      <td>0.003471</td>\n",
       "      <td>0.004093</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.003616</td>\n",
       "      <td>0.007156</td>\n",
       "      <td>0.090434</td>\n",
       "      <td>0.123022</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041311</td>\n",
       "      <td>0.087843</td>\n",
       "      <td>0.004395</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.096796</td>\n",
       "      <td>0.041162</td>\n",
       "      <td>0.057836</td>\n",
       "      <td>0.006326</td>\n",
       "      <td>0.032747</td>\n",
       "      <td>-0.003174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市销率(TTM)</th>\n",
       "      <td>0.017226</td>\n",
       "      <td>0.017424</td>\n",
       "      <td>0.017081</td>\n",
       "      <td>0.017280</td>\n",
       "      <td>-0.005195</td>\n",
       "      <td>0.019108</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.043696</td>\n",
       "      <td>0.005765</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001689</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.017525</td>\n",
       "      <td>0.016314</td>\n",
       "      <td>0.010450</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>0.005801</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.024195</td>\n",
       "      <td>-0.003304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACD_y</th>\n",
       "      <td>0.123004</td>\n",
       "      <td>0.124033</td>\n",
       "      <td>0.121631</td>\n",
       "      <td>0.122960</td>\n",
       "      <td>0.035608</td>\n",
       "      <td>0.134428</td>\n",
       "      <td>0.053387</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.036979</td>\n",
       "      <td>0.000445</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099579</td>\n",
       "      <td>0.246860</td>\n",
       "      <td>0.095510</td>\n",
       "      <td>0.089001</td>\n",
       "      <td>0.266537</td>\n",
       "      <td>0.038280</td>\n",
       "      <td>0.111081</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>0.171240</td>\n",
       "      <td>-0.003339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总股本(万股)</th>\n",
       "      <td>0.180834</td>\n",
       "      <td>0.180482</td>\n",
       "      <td>0.181148</td>\n",
       "      <td>0.180788</td>\n",
       "      <td>0.549382</td>\n",
       "      <td>0.328206</td>\n",
       "      <td>-0.117436</td>\n",
       "      <td>-0.007130</td>\n",
       "      <td>-0.082414</td>\n",
       "      <td>-0.006091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013469</td>\n",
       "      <td>-0.017619</td>\n",
       "      <td>0.180290</td>\n",
       "      <td>0.182842</td>\n",
       "      <td>-0.016038</td>\n",
       "      <td>-0.008268</td>\n",
       "      <td>-0.011244</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.139896</td>\n",
       "      <td>-0.003693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACD_x</th>\n",
       "      <td>0.116551</td>\n",
       "      <td>0.118230</td>\n",
       "      <td>0.115920</td>\n",
       "      <td>0.117618</td>\n",
       "      <td>0.041626</td>\n",
       "      <td>0.139980</td>\n",
       "      <td>0.060334</td>\n",
       "      <td>-0.002692</td>\n",
       "      <td>0.034791</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161441</td>\n",
       "      <td>0.275209</td>\n",
       "      <td>0.077082</td>\n",
       "      <td>0.071742</td>\n",
       "      <td>0.277114</td>\n",
       "      <td>0.098600</td>\n",
       "      <td>0.181871</td>\n",
       "      <td>0.047550</td>\n",
       "      <td>0.163784</td>\n",
       "      <td>-0.003733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>流通市值(万元)</th>\n",
       "      <td>0.625955</td>\n",
       "      <td>0.626673</td>\n",
       "      <td>0.625600</td>\n",
       "      <td>0.626344</td>\n",
       "      <td>0.210309</td>\n",
       "      <td>0.629890</td>\n",
       "      <td>-0.081711</td>\n",
       "      <td>-0.009037</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>-0.005316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023288</td>\n",
       "      <td>0.054987</td>\n",
       "      <td>0.626383</td>\n",
       "      <td>0.622082</td>\n",
       "      <td>0.034744</td>\n",
       "      <td>0.013157</td>\n",
       "      <td>0.018590</td>\n",
       "      <td>0.025058</td>\n",
       "      <td>0.574701</td>\n",
       "      <td>-0.003741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总市值(万元)</th>\n",
       "      <td>0.584770</td>\n",
       "      <td>0.585517</td>\n",
       "      <td>0.584357</td>\n",
       "      <td>0.585136</td>\n",
       "      <td>0.195478</td>\n",
       "      <td>0.656427</td>\n",
       "      <td>-0.074816</td>\n",
       "      <td>-0.009252</td>\n",
       "      <td>0.014539</td>\n",
       "      <td>-0.004445</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024326</td>\n",
       "      <td>0.057995</td>\n",
       "      <td>0.585388</td>\n",
       "      <td>0.580893</td>\n",
       "      <td>0.038219</td>\n",
       "      <td>0.014363</td>\n",
       "      <td>0.020391</td>\n",
       "      <td>0.023509</td>\n",
       "      <td>0.541124</td>\n",
       "      <td>-0.004083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市销率</th>\n",
       "      <td>0.048711</td>\n",
       "      <td>0.049237</td>\n",
       "      <td>0.048312</td>\n",
       "      <td>0.048835</td>\n",
       "      <td>-0.009367</td>\n",
       "      <td>0.061859</td>\n",
       "      <td>0.030107</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.114168</td>\n",
       "      <td>0.006523</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016031</td>\n",
       "      <td>0.035207</td>\n",
       "      <td>0.049585</td>\n",
       "      <td>0.046316</td>\n",
       "      <td>0.034653</td>\n",
       "      <td>0.015483</td>\n",
       "      <td>0.021917</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>0.067224</td>\n",
       "      <td>-0.004383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市净率</th>\n",
       "      <td>0.187223</td>\n",
       "      <td>0.188772</td>\n",
       "      <td>0.186007</td>\n",
       "      <td>0.187601</td>\n",
       "      <td>-0.010438</td>\n",
       "      <td>0.258015</td>\n",
       "      <td>0.096361</td>\n",
       "      <td>-0.002607</td>\n",
       "      <td>0.194233</td>\n",
       "      <td>0.011052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065370</td>\n",
       "      <td>0.142935</td>\n",
       "      <td>0.189402</td>\n",
       "      <td>0.179790</td>\n",
       "      <td>0.131769</td>\n",
       "      <td>0.053552</td>\n",
       "      <td>0.075740</td>\n",
       "      <td>0.018774</td>\n",
       "      <td>0.237258</td>\n",
       "      <td>-0.006275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_30</th>\n",
       "      <td>0.026948</td>\n",
       "      <td>0.028503</td>\n",
       "      <td>0.026666</td>\n",
       "      <td>0.028104</td>\n",
       "      <td>0.197277</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.402352</td>\n",
       "      <td>0.048775</td>\n",
       "      <td>0.096796</td>\n",
       "      <td>-0.001574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.491441</td>\n",
       "      <td>0.799439</td>\n",
       "      <td>0.019214</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394584</td>\n",
       "      <td>0.567529</td>\n",
       "      <td>0.050211</td>\n",
       "      <td>0.073110</td>\n",
       "      <td>-0.007671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>0.069282</td>\n",
       "      <td>0.070376</td>\n",
       "      <td>0.068782</td>\n",
       "      <td>0.069728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.590246</td>\n",
       "      <td>0.291791</td>\n",
       "      <td>0.126709</td>\n",
       "      <td>0.007156</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132404</td>\n",
       "      <td>0.186115</td>\n",
       "      <td>0.069726</td>\n",
       "      <td>0.065252</td>\n",
       "      <td>0.197277</td>\n",
       "      <td>0.142120</td>\n",
       "      <td>0.169580</td>\n",
       "      <td>0.020167</td>\n",
       "      <td>0.103220</td>\n",
       "      <td>-0.010789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成交额(千元)</th>\n",
       "      <td>0.429777</td>\n",
       "      <td>0.433146</td>\n",
       "      <td>0.427705</td>\n",
       "      <td>0.430862</td>\n",
       "      <td>0.590246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283440</td>\n",
       "      <td>0.101805</td>\n",
       "      <td>0.090434</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145176</td>\n",
       "      <td>0.228716</td>\n",
       "      <td>0.433119</td>\n",
       "      <td>0.417035</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.152843</td>\n",
       "      <td>0.189296</td>\n",
       "      <td>0.052441</td>\n",
       "      <td>0.520673</td>\n",
       "      <td>-0.011780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>量比</th>\n",
       "      <td>-0.006801</td>\n",
       "      <td>-0.005488</td>\n",
       "      <td>-0.007179</td>\n",
       "      <td>-0.005987</td>\n",
       "      <td>0.126709</td>\n",
       "      <td>0.101805</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224125</td>\n",
       "      <td>0.112938</td>\n",
       "      <td>-0.007339</td>\n",
       "      <td>-0.007344</td>\n",
       "      <td>0.048775</td>\n",
       "      <td>0.234986</td>\n",
       "      <td>0.140443</td>\n",
       "      <td>0.034984</td>\n",
       "      <td>0.039641</td>\n",
       "      <td>-0.014275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>换手率(%)</th>\n",
       "      <td>-0.041899</td>\n",
       "      <td>-0.040521</td>\n",
       "      <td>-0.042639</td>\n",
       "      <td>-0.041424</td>\n",
       "      <td>0.291791</td>\n",
       "      <td>0.283440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>0.123022</td>\n",
       "      <td>0.011780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254396</td>\n",
       "      <td>0.354580</td>\n",
       "      <td>-0.041216</td>\n",
       "      <td>-0.048088</td>\n",
       "      <td>0.402352</td>\n",
       "      <td>0.291029</td>\n",
       "      <td>0.350873</td>\n",
       "      <td>0.019925</td>\n",
       "      <td>0.020307</td>\n",
       "      <td>-0.030243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open      high       low     close    volume   成交额(千元)  \\\n",
       "return_s1   -0.002781 -0.002701 -0.002671 -0.002690 -0.010789 -0.011780   \n",
       "rsi_6        0.016514  0.018744  0.017765  0.019651  0.132404  0.145176   \n",
       "return_5     0.008235  0.010136  0.009223  0.010894  0.142120  0.152843   \n",
       "股息率(%)       0.025713  0.025004  0.026291  0.025584 -0.015134 -0.084152   \n",
       "return_10    0.013507  0.015153  0.013945  0.015370  0.169580  0.189296   \n",
       "close-o      0.001610  0.015036  0.012924  0.024808  0.020167  0.052441   \n",
       "股息率(TTM)(%)  0.024797  0.024083  0.025357  0.024658 -0.012740 -0.079034   \n",
       "市盈率(TTM)    -0.003790 -0.003778 -0.003795 -0.003784  0.000049 -0.001229   \n",
       "市盈率(动态)     -0.006395 -0.006164 -0.006569 -0.006343 -0.004149  0.022648   \n",
       "dong10       0.056435  0.060502  0.059289  0.062556  0.038306  0.097396   \n",
       "rsi_24       0.042869  0.044697  0.043051  0.044669  0.186115  0.228716   \n",
       "MACD_z       0.003197  0.005487  0.005313  0.006759  0.026259  0.043987   \n",
       "b_high       0.997486  0.997522  0.997255  0.997311  0.069426  0.432747   \n",
       "SMA20_close  0.998256  0.998108  0.998190  0.998055  0.067122  0.424657   \n",
       "b_low        0.996973  0.996610  0.997098  0.996741  0.064283  0.414273   \n",
       "up           0.997836  0.997894  0.997635  0.997707  0.069726  0.433119   \n",
       "low          0.999878  0.999832  1.000000  0.999872  0.068782  0.427705   \n",
       "close        0.999731  0.999896  0.999872  1.000000  0.069728  0.430862   \n",
       "down         0.997760  0.997453  0.997871  0.997574  0.065252  0.417035   \n",
       "high         0.999853  1.000000  0.999832  0.999896  0.070376  0.433146   \n",
       "open         1.000000  0.999853  0.999878  0.999731  0.069282  0.429777   \n",
       "流通股本(万股)     0.186648  0.186291  0.186977  0.186607  0.567982  0.332313   \n",
       "high-l       0.871337  0.876535  0.867567  0.872661  0.103220  0.520673   \n",
       "市盈率(静态)      0.003471  0.004093  0.002982  0.003616  0.007156  0.090434   \n",
       "市销率(TTM)     0.017226  0.017424  0.017081  0.017280 -0.005195  0.019108   \n",
       "MACD_y       0.123004  0.124033  0.121631  0.122960  0.035608  0.134428   \n",
       "总股本(万股)      0.180834  0.180482  0.181148  0.180788  0.549382  0.328206   \n",
       "MACD_x       0.116551  0.118230  0.115920  0.117618  0.041626  0.139980   \n",
       "流通市值(万元)     0.625955  0.626673  0.625600  0.626344  0.210309  0.629890   \n",
       "总市值(万元)      0.584770  0.585517  0.584357  0.585136  0.195478  0.656427   \n",
       "市销率          0.048711  0.049237  0.048312  0.048835 -0.009367  0.061859   \n",
       "市净率          0.187223  0.188772  0.186007  0.187601 -0.010438  0.258015   \n",
       "return_30    0.026948  0.028503  0.026666  0.028104  0.197277  0.252304   \n",
       "volume       0.069282  0.070376  0.068782  0.069728  1.000000  0.590246   \n",
       "成交额(千元)      0.429777  0.433146  0.427705  0.430862  0.590246  1.000000   \n",
       "量比          -0.006801 -0.005488 -0.007179 -0.005987  0.126709  0.101805   \n",
       "换手率(%)      -0.041899 -0.040521 -0.042639 -0.041424  0.291791  0.283440   \n",
       "\n",
       "               换手率(%)        量比   市盈率(静态)  市盈率(TTM)  ...     rsi_6    rsi_24  \\\n",
       "return_s1   -0.030243 -0.014275 -0.003174 -0.000616  ...  0.012287 -0.001576   \n",
       "rsi_6        0.254396  0.224125  0.041311  0.000531  ...  1.000000  0.768101   \n",
       "return_5     0.291029  0.234986  0.041162  0.001682  ...  0.775536  0.566387   \n",
       "股息率(%)      -0.137976 -0.000943 -0.203916 -0.012941  ... -0.032199 -0.066985   \n",
       "return_10    0.350873  0.140443  0.057836  0.000842  ...  0.765123  0.703014   \n",
       "close-o      0.019925  0.034984  0.006326  0.000228  ...  0.135416  0.078153   \n",
       "股息率(TTM)(%) -0.135914 -0.003163 -0.214485 -0.015713  ... -0.043280 -0.082046   \n",
       "市盈率(TTM)     0.011780  0.001505  0.068595  1.000000  ...  0.000531 -0.000388   \n",
       "市盈率(动态)      0.050468  0.002822  0.199799  0.013479  ...  0.018051  0.033446   \n",
       "dong10       0.050601  0.019326  0.017272  0.000340  ...  0.241366  0.219087   \n",
       "rsi_24       0.354580  0.112938  0.087843 -0.000388  ...  0.768101  1.000000   \n",
       "MACD_z       0.032702  0.018299  0.000167  0.000167  ...  0.218099  0.139088   \n",
       "b_high      -0.041526 -0.007848  0.004438 -0.003782  ...  0.003925  0.031151   \n",
       "SMA20_close -0.045227 -0.007531  0.002298 -0.003820  ...  0.003218  0.028858   \n",
       "b_low       -0.049454 -0.007144 -0.000208 -0.003856  ...  0.002385  0.026116   \n",
       "up          -0.041216 -0.007339  0.004395 -0.003782  ...  0.005206  0.032079   \n",
       "low         -0.042639 -0.007179  0.002982 -0.003795  ...  0.017765  0.043051   \n",
       "close       -0.041424 -0.005987  0.003616 -0.003784  ...  0.019651  0.044669   \n",
       "down        -0.048088 -0.007344  0.000331 -0.003846  ...  0.004033  0.027955   \n",
       "high        -0.040521 -0.005488  0.004093 -0.003778  ...  0.018744  0.044697   \n",
       "open        -0.041899 -0.006801  0.003471 -0.003790  ...  0.016514  0.042869   \n",
       "流通股本(万股)    -0.117960 -0.007085 -0.078500 -0.007835  ... -0.011050 -0.013093   \n",
       "high-l       0.020307  0.039641  0.032747 -0.002832  ...  0.042053  0.082214   \n",
       "市盈率(静态)      0.123022  0.003462  1.000000  0.068595  ...  0.041311  0.087843   \n",
       "市销率(TTM)     0.009917  0.000427  0.043696  0.005765  ... -0.001689  0.002847   \n",
       "MACD_y       0.053387 -0.008929  0.036979  0.000445  ...  0.099579  0.246860   \n",
       "总股本(万股)     -0.117436 -0.007130 -0.082414 -0.006091  ... -0.013469 -0.017619   \n",
       "MACD_x       0.060334 -0.002692  0.034791  0.000470  ...  0.161441  0.275209   \n",
       "流通市值(万元)    -0.081711 -0.009037  0.000442 -0.005316  ...  0.023288  0.054987   \n",
       "总市值(万元)     -0.074816 -0.009252  0.014539 -0.004445  ...  0.024326  0.057995   \n",
       "市销率          0.030107  0.000994  0.114168  0.006523  ...  0.016031  0.035207   \n",
       "市净率          0.096361 -0.002607  0.194233  0.011052  ...  0.065370  0.142935   \n",
       "return_30    0.402352  0.048775  0.096796 -0.001574  ...  0.491441  0.799439   \n",
       "volume       0.291791  0.126709  0.007156  0.000049  ...  0.132404  0.186115   \n",
       "成交额(千元)      0.283440  0.101805  0.090434 -0.001229  ...  0.145176  0.228716   \n",
       "量比           0.256570  1.000000  0.003462  0.001505  ...  0.224125  0.112938   \n",
       "换手率(%)       1.000000  0.256570  0.123022  0.011780  ...  0.254396  0.354580   \n",
       "\n",
       "                   up      down  return_30  return_5  return_10   close-o  \\\n",
       "return_s1   -0.002655 -0.002691  -0.007671  0.008091   0.003990  0.003882   \n",
       "rsi_6        0.005206  0.004033   0.491441  0.775536   0.765123  0.135416   \n",
       "return_5    -0.000401 -0.002035   0.394584  1.000000   0.700945  0.114734   \n",
       "股息率(%)       0.024366  0.029261  -0.063777 -0.029812  -0.041034 -0.005181   \n",
       "return_10    0.002090 -0.000972   0.567529  0.700945   1.000000  0.080467   \n",
       "close-o      0.007611  0.005140   0.050211  0.114734   0.080467  1.000000   \n",
       "股息率(TTM)(%)  0.023466  0.028307  -0.074283 -0.036254  -0.048661 -0.005688   \n",
       "市盈率(TTM)    -0.003782 -0.003846  -0.001574  0.001682   0.000842  0.000228   \n",
       "市盈率(动态)     -0.006166 -0.007625   0.043888  0.021153   0.030090  0.002159   \n",
       "dong10       0.012619  0.011342   0.169959  0.202761   0.289318  0.264635   \n",
       "rsi_24       0.032079  0.027955   0.799439  0.566387   0.703014  0.078153   \n",
       "MACD_z      -0.040622 -0.038130   0.085826  0.201221   0.249019  0.153552   \n",
       "b_high       0.999837  0.996886   0.018881 -0.002157   0.000449  0.005673   \n",
       "SMA20_close  0.999242  0.999210   0.015197 -0.003116  -0.001442  0.004521   \n",
       "b_low        0.996359  0.999739   0.010859 -0.004229  -0.003649  0.003166   \n",
       "up           1.000000  0.997371   0.019214 -0.000401   0.002090  0.007611   \n",
       "low          0.997635  0.997871   0.026666  0.009223   0.013945  0.012924   \n",
       "close        0.997707  0.997574   0.028104  0.010894   0.015370  0.024808   \n",
       "down         0.997371  1.000000   0.012625 -0.002035  -0.000972  0.005140   \n",
       "high         0.997894  0.997453   0.028503  0.010136   0.015153  0.015036   \n",
       "open         0.997836  0.997760   0.026948  0.008235   0.013507  0.001610   \n",
       "流通股本(万股)     0.186033  0.188628  -0.012693 -0.006731  -0.009172  0.000678   \n",
       "high-l       0.877082  0.858936   0.073110  0.032832   0.044950  0.068582   \n",
       "市盈率(静态)      0.004395  0.000331   0.096796  0.041162   0.057836  0.006326   \n",
       "市销率(TTM)     0.017525  0.016314   0.010450  0.003877   0.005801  0.002572   \n",
       "MACD_y       0.095510  0.089001   0.266537  0.038280   0.111081 -0.000266   \n",
       "总股本(万股)      0.180290  0.182842  -0.016038 -0.008268  -0.011244  0.000416   \n",
       "MACD_x       0.077082  0.071742   0.277114  0.098600   0.181871  0.047550   \n",
       "流通市值(万元)     0.626383  0.622082   0.034744  0.013157   0.018590  0.025058   \n",
       "总市值(万元)      0.585388  0.580893   0.038219  0.014363   0.020391  0.023509   \n",
       "市销率          0.049585  0.046316   0.034653  0.015483   0.021917  0.006003   \n",
       "市净率          0.189402  0.179790   0.131769  0.053552   0.075740  0.018774   \n",
       "return_30    0.019214  0.012625   1.000000  0.394584   0.567529  0.050211   \n",
       "volume       0.069726  0.065252   0.197277  0.142120   0.169580  0.020167   \n",
       "成交额(千元)      0.433119  0.417035   0.252304  0.152843   0.189296  0.052441   \n",
       "量比          -0.007339 -0.007344   0.048775  0.234986   0.140443  0.034984   \n",
       "换手率(%)      -0.041216 -0.048088   0.402352  0.291029   0.350873  0.019925   \n",
       "\n",
       "               high-l  return_s1  \n",
       "return_s1   -0.003159   1.000000  \n",
       "rsi_6        0.042053   0.012287  \n",
       "return_5     0.032832   0.008091  \n",
       "股息率(%)      -0.011977   0.006233  \n",
       "return_10    0.044950   0.003990  \n",
       "close-o      0.068582   0.003882  \n",
       "股息率(TTM)(%) -0.012458   0.003668  \n",
       "市盈率(TTM)    -0.002832  -0.000616  \n",
       "市盈率(动态)      0.005257  -0.000868  \n",
       "dong10       0.084648  -0.001090  \n",
       "rsi_24       0.082214  -0.001576  \n",
       "MACD_z       0.009358  -0.001915  \n",
       "b_high       0.876977  -0.002563  \n",
       "SMA20_close  0.868338  -0.002600  \n",
       "b_low        0.856341  -0.002639  \n",
       "up           0.877082  -0.002655  \n",
       "low          0.867567  -0.002671  \n",
       "close        0.872661  -0.002690  \n",
       "down         0.858936  -0.002691  \n",
       "high         0.876535  -0.002701  \n",
       "open         0.871337  -0.002781  \n",
       "流通股本(万股)     0.144462  -0.002911  \n",
       "high-l       1.000000  -0.003159  \n",
       "市盈率(静态)      0.032747  -0.003174  \n",
       "市销率(TTM)     0.024195  -0.003304  \n",
       "MACD_y       0.171240  -0.003339  \n",
       "总股本(万股)      0.139896  -0.003693  \n",
       "MACD_x       0.163784  -0.003733  \n",
       "流通市值(万元)     0.574701  -0.003741  \n",
       "总市值(万元)      0.541124  -0.004083  \n",
       "市销率          0.067224  -0.004383  \n",
       "市净率          0.237258  -0.006275  \n",
       "return_30    0.073110  -0.007671  \n",
       "volume       0.103220  -0.010789  \n",
       "成交额(千元)      0.520673  -0.011780  \n",
       "量比           0.039641  -0.014275  \n",
       "换手率(%)       0.020307  -0.030243  \n",
       "\n",
       "[37 rows x 37 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te_corr.sort_values(by='return_s1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(te_corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e+/3335eaNWtan4eHh0vx4sXN423btsnp06dN6M6aNau89dZbMcI/AAAAAMBzUen+/9aToKAgu32xtXlHRkZK4cKFrc91LHfq1Knl8uXLki1btgTf588//5SFCxfKH3/8YQ3gtWvXlpCQELly5Yq8/vrr5ts4DeoA4Ei02AEAALgGoTuOVvLYpEmTJsZx+j+WOjY7odD98OFD6dy5s5l8rWzZsmbfpEmT7I75+OOP5fPPPyd0A3A4WuwAAABcg/byJNCJ0M6fP2+379q1a5IuXboEz/3kk0/k0qVLMnLkyDiPyZ07t5w5c8Yh9woAAAAAcD1CdxJUrVpVtm7dan1+7NgxMwZbw3h8li5daiZf++GHH6zjwZWO9daJ1iz02gULFnTS3QMAAAAAkhuhOwnq1KljxnVPmzbNPNfZyxs0aGDGdeuY7AcPHsQ458CBA/Lqq6/KuHHjJH/+/HL9+nXTjq60zbxr167y22+/mRnOP/30UzOZGgAAAADAOxC6k0DHdE+ZMkW6d+8uOXPmlMWLF8vw4cPNazqme+/evTHO+eqrr+TGjRvSoUMHyZw5s9nKlCljXhs1apQZI16/fn0ZMGCAaT3X4wAAAAAA3oGJ1JKoRYsWcuTIEdmxY4dZPixHjhxmf1RUVKzH68RFcU1epMuE6WzmAAAAAADvROh+BHny5JGmTZu6+jYAAAAAAG6O9nIAAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CUuGAQAAIFkFBwfL7du3nf4+ERER0rt3b6e+h5+fn4SGhjr1PQB4NkI3AAAAkpUG7jFjxog3cHaoB+D5CN0AACSh+pbYyhnVLwAAoAjdAAA4ofpG9QsAACgmUgMAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAACchNANAAAAAICTELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QAAAAAAOEkaZ10YAAAAjhMcHCy3b9+O95iIiAjp3bt3gtfy8/OT0NBQB94dACAuhG4AAAAPoIF7zJgxDrlWYoI5AMAxaC8HAAAAAMBJqHQjRUtMq567SGzLoKvRsggAKcej/nf0Uf+bltz/jaGlH4AjELqRojmyVQ//8IQvBgAAnvnf0eT+bwwt/QAcgfbyJAoLC5OqVatKtmzZ5L333pOoqKgEz5k/f74ULFhQnnzySZkzZ47da1988YUEBARIkSJFZO3atU68cwAAAABAcqPSnQR37tyR5s2bS6NGjWTu3LnSo0cPmT59unTq1CnekP7666+bcF29enVp1aqVVKpUSUqWLCkrV66Uvn37mmvlypVL2rVrJ9u3b5ccOXIk6+cCAADOaSumpRgAQOj+/zCtmy1fX1+z2Vq+fLlcvXpVRo8eLRkyZJChQ4fKO++8E2/onjJlitSvX1+6dOlinnfv3l2++eYbGTx4sEycOFE6dOggLVu2NK/pz4ULF1qPBQAAruGotmJaihOP8dMAvNVjhe58ty6KNwgNnSYDBw602zdgwAAJCQmx27d7926pUaOGCdwqMDBQ9u/fH++19Zznn3/e+rxatWoyaNAg62uvvfaa3Wu//PILoRsAAKQ4jJ8G4K2odP//N6tBQUF2+6JXuVVkZKQULlzY+tzHx0dSp04tly9fNmO8YxP9HH9/fzl79myCrwEAAAAAPB+hO45W8tikSZMmxnHavnTz5s04Q3f0cyzHJ/QaAAAA4CmYBwGIG6E7CbJnz24mRrN17do1SZcuXbznnD9/Ptbj43sNAAAA8BTMgwDEjSXDkkCXCtu6dav1+bFjx8wEbBqeE3vOzp07JW/evAm+BgAAAADwfITuJKhTp44Zhz1t2jTzXGcvb9CggRnXfeXKFXnw4EGMc1q3bm2WBNu7d69cv35dPv/8c7PkmHrppZdkwoQJcubMGTl37pxMnTrV+hoAAAAAwPMRupNAx2DrEmC67FfOnDll8eLFMnz4cPOajunWYB1dhQoVpGfPnlKlShVTxdaA/vbbb5vXdM3vf/3rX1K8eHEzoVrFihXNOt4AAAAAAO/AmO4katGihRw5ckR27Nhhlg/LkSOH2R8VFRXnOUOGDJHXX3/dVLTr1q1rHbets5/rmt09evSQGzdumNd0HwAAAADAOxC6H0GePHmkadOmSTqnTJkyZouNju0GAAAAAHgf2ssBAAAAAHASQjcAAAAAAE5CezkAINGCg4PNWqwJiYiISHCtVT8/PwkNDXXg3QEAALgfQjcAINE0cI8ZM8Yh10oolAMAAHgD2ssBAAAAAHASKt2Al7f5JrfEtBW7Cu3MAAAASG6EbsBDObLNN6Vw1y8DAAAA4L0I3QAAAADwmB2GTCKKuBC6AQAAACAZOgzpukuZCN0AUsyY9fi+gT5w4IA8fPgwydfMlSuX5M6dO8nn8U03AABAyuATFRUV9agnR+z9Q7xB7vKVXH0LAAAAAAAvRKUbXqteyHhX3wLgdA0DS4g3axxY0tW34DHy3boo7uZ0+hyuvgUAXvjvlpTw75dKxQq6+hbgQKzTDQAAAACAkxC6AQAAAABwEkI3AAAAAABOQugGAAAAAMBJCN0AAAAA4IHWr18vhQoVivP148ePi4+PT6KvN336dKlXr57T7ielInQDAAAAgBcqUKCAXL582dW3keIRugEAAADAC6VKlUqyZs3q6ttI8QjdAAAAAOBG7ty5I5GRkXab7ovLkiVLpGDBgpI9e3YZP358gu3le/bskXLlypnje/fuLaVKlZJx48ZZX588ebIEBASYbcGCBU74hClLGlffAFKm4OBguX37ttOuP2bMGKddGwAAAHCm0NBQGThwoN2+AQMGSEhISIxjL168KMOHD5dly5bJunXrpE+fPtKlSxfx8/OL8/rdunWTV199VZo1aybPPPOMLF++XMqUKSOLFy+WsLAwE7Q3b94sU6ZMkV69ekmrVq2kUqVKcvTo0RjXmjlzprRo0cJBn9w7EbrhEhq4CcYAAABA7AWqoKAgu32+vr6xHnv9+nWZOHGilC1bVooXLy7vvvuunDt3zlS+47Jr1y4zaVqJEiXMeSdPnpSnn37avHbjxg2ZMWOG5M6dWzp37mwCvVq6dKncu3cvxrX0OMSP0A0AAAAAbkQDdlwhO7ps2bJJYGCgeZwuXTrzMyoqKt5zihUrJlu3bpWcOXPKoUOHTJXbonTp0tYgbbmeyps37yN9Fjxm6M5dvpLj7gQAAAAAkCT+/v5JOl4DuYbsHj16yH/+8x/p3r27VKhQ4ZGvh4RR6QYAAACAFCI8PFw2btxoxmxnyZJF8ufP7+pb8nrMXg4AAAAAKYSO49b28bp160rhwoUlffr0ZmI1OA+hGwAAAAA8UL169cyyYNHbxwsVKmQe68/o47unTZtmQrdOpqYTrums5zpxmi5L1rFjR1m/fr312NjOT+r9gPZyAAAAAEgx6tevL7NnzzbrdN+6dcvMcj5s2DDGcjsRoRsAAAAAUogiRYrImjVrXH0bKQqhGwAAuL18ty465Dqn0+dwyHUAd/5z7kj8nQEeH2O6AQAAAABwEkI3AAAAAABOQugGAAAAAMBJCN0AAAAAADgJoRsAAAAAACchdAMAAACAB1q/fr0UKlQoztePHz8uPj4+ib7e9OnTpV69eg66O1gQugEAAADACxUoUEAuX77s6ttI8QjdAAAAAOCFUqVKJVmzZnX1baR4hG4AAAAAcCN37tyRyMhIu033xWXJkiVSsGBByZ49u4wfPz7B9vI9e/ZIuXLlzPG9e/eWUqVKybhx46yvT548WQICAsy2YMECJ3zClIXQDQAAAABuJDQ0VLJkyWK36b7YXLx4UYYPHy7Lli2TQYMGSZ8+feT27dvxXr9bt27y6quvyrp162Tq1Klma9eunXktLCzMBO3NmzdLp06dpFevXk75jCkJoRsAAAAA3EhwcLBcvXrVbtN9sbl+/bpMnDhRypYtK2+++abcvXtXzp07F+/1d+3aJS+//LJUqFDBnHfy5EnJli2bee3GjRsyY8YMKVasmHTu3FlOnTrllM+YkqRx9Q0AAAAAAP7H19fXbImhYTkwMNA8TpcunfkZFRUV7zkaqLdu3So5c+aUQ4cOSZkyZayvlS5dWnLnzm13PTweKt0AAAAA4KH8/f2TdLwGcg3ZPXr0kDx58kj79u1NxftRr4eEUekGAAAAgBQiPDxcNm7caMZs61jx/Pnzu/qWvB6VbgAAAABIIUqUKGHax+vWrSuFCxeW9OnTm4nV4DyEbgAAAADwQPXq1TPLgkVvHy9UqJB5rD+jj++eNm2aCd06mZpOuKaznuvEabosWceOHWX9+vXWY2M7H0lHezkAAAAApBD169eX2bNnm3W6b926Zdb3HjZsGGO5nYjQDQAAAAApRJEiRWTNmjWuvo0UhdANAACcIt+ti66+BQAAXI4x3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAACchNANAAAAAF5I1/D28fFx9W2keIRuAAAAAACchNANAAAAAICTELoBAAAAwI3cuXNHIiMj7TbdF5udO3dKzZo1JVOmTPL000/Lvn37Erx+WFiY1K5dW7JkySJNmjSR06dPW19bvXq1lClTRjJkyCC1atWSw4cPW19bsWKFlC9fXrJmzSpdunSJ855gj9ANAAAAAG4kNDTUBGLbTfdFd/XqVWncuLE0bdpUwsPDpWrVqvL666/He+3r169Lw4YN5bnnnpM9e/ZI/vz5pWXLlvLw4UPzert27aRTp07mehq++/fvb/Zr+NbjevbsKdu3b5dt27bJyJEjnfQb8C5pXH0DAAAAAID/CQ4OlqCgILt9vr6+MY776aefJFu2bPLRRx+Z5wMGDDBV7/gsXbpUMmfObI5VY8eOlVy5cpkQXaNGDUmfPr3cu3dPsmfPLl999ZXcv3/fHPfdd9/JU089ZSrc6u2335avv/7a+t6IG5VuAAAAAHAjGrD9/f3ttthC96lTp6Rw4cLW5xrA27RpE++1o5/j5+cnefPmlZMnT5rns2bNknXr1pl99evXN63oSlvQtZVdW8t169u3r/UcxI/QDQAAAAAeSFvDdVkw29bxcuXKyd9//x3nOQUKFJBjx45Zn+u47LNnz0rBggXl5s2bprL9888/y4ULF8y4744dO5rj8uXLJ82bN5ddu3aZbffu3eY4JIzQDQAAAAAeSMdyX7p0yYz31kr04MGD5cGDBxIQEBDnOc2aNZNr167JwIED5cSJE9KjRw8pXry4GQ+ugbtRo0by7bffSkREhERFRVnby9u2bSsbN26UQ4cOmar7uHHjzNhvJIzQDQAAAAAeSCdY0xnFlyxZIqVLl5Zff/1VFi5cKD4+PnGeo7Ocr1y5UlatWmVmItcW8cWLF0uqVKlMG7u2l2t4L1q0qBn/PWnSJHOePp85c6YZa16sWDEzCducOXOS8dN6Lp8o/foCbjVpwu3bt8Xb6Tdn+g2aM9ULGe/U6wPuoGFgCfFmjQNLuvoWPEa+WxddfQse4XT6HK6+BcCj/h7zd8Y1KhUr6OpbgAMxe7mb0cA9ZswY8Xa9e/d29S0AAAAAgNPRXg4AAAAAgJMQugEAAAAAcBLay+GV4+BTQos+AM/njuM34dl/Fhh/CwDuh9ANj5dSxsEDAAAA8Dy0lwMAAAAA4CSEbgAAAAAAnITQDQAAAACAkxC6AQAAAABwEkI3AAAAAABOQugGAAAAAMBJCN0AAAAA4Ebu3LkjkZGRdpvui01YWJjUrl1bsmTJIk2aNJHTp0/L9OnTpVq1atKyZUuzv3HjxvLXX39Zz9m+fbtUr17dvNaqVSu5evWq2a/n1atXTyZPniwBAQFmW7BgQbJ9bm9F6AYAAAAANxIaGmoCse2m+6K7fv26NGzYUJ577jnZs2eP5M+f3wTthw8fmmBds2ZN2bVrl/j6+kq3bt3MOVeuXJHnn3/ebHqOBvo+ffrYhXgN2ps3b5ZOnTpJr169kvWze6M0rr4BAAAAAMD/BAcHS1BQkN0+Dc7RLV26VDJnziwDBgwwz8eOHSu5cuWSiIgIyZcvn/Tr1098fHwkJCREqlatKvfv35effvpJ0qZNa87R1/r27Svt27e3XvPGjRsyY8YMyZ07t3Tu3FmGDx+eDJ/YuxG6AQAAAMCNaMCOLWRHd+rUKSlcuLD1uZ+fn+TNm1dSpUplQreGaqX7Hjx4IBcvXjTt5+fPn5ds2bKZ17Qqfu3aNbl9+7Z5Xrp0aRO4Vbp06Zz0CVMWQjcAAAAAeKACBQrIsWPHrM913PfZs2clKipKTp48aX5q8NZwniZNGsmZM6cJ45UrV5bvvvvOnKPH6JhurX4rf39/l30eb8WYbgAAAADwQM2aNTNV6oEDB8qJEyekR48eUrx4cdNiruFbx4FrKNfXdax36tSppWnTpiaQb9u2TdKnTy/z5883E61p+IZzELoBAAAAwANlypRJVq5cKatWrZLy5cubML148WLTXl6jRg0TrMuVKyd3796V8ePHm3OyZs0qS5YskU8//VSKFCki33//vXmulXA4B79ZAAAAAPBQGrZ1pvHodEz4okWLYj1HJ1X77bffYuzv2LGj2SwKFSpEBdwBqHQDAAAAAOAkhG4AAAAA8CJarV6/fr2rbwP/j/ZyAABc5HT6HA67Vr5bFx12LXj2nwUAgHuh0g0AAAAAgJMQugEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbAAAAADzY8ePHxcfHx9W3gTgQugEAAAAAcBJCNwAAAAAATkLoBgAAAAA3cufOHYmMjLTbdF9ShIWFSe3atSVLlizSpEkTOX36tNlfunRpWbt2rSxatEhSpUolt27dkvfee0/effddJ30aELoBAAAAwI2EhoaasGy76b7Eun79ujRs2FCee+452bNnj+TPn19atmwpDx8+lIoVK8qhQ4fkwIEDUqtWLQkPDzfPK1Wq5NTPlJKlcfUNAAAAAAD+Jzg4WIKCguz2+fr6Jvr8pUuXSubMmWXAgAHm+dixYyVXrlyybds2a+g+f/68NGvWTA4ePEjodjIq3QAAAADgRjRg+/v7221JCd2nTp2SwoULW5/7+flJ3rx55eTJk9bQre3mderUkX379smZM2ekbNmyTvo0IHQDAAAAgBcpUKCAHDt2zPpcx4OfPXtWChYsaEK3tpZrq3nx4sVl+fLlUqxYMUmThiZoZyF0AwAAAIAX0bbxa9euycCBA+XEiRPSo0cPE7CrVq0qOXLkkNu3b0tAQIBpOT98+DCt5U5G6AYAAAAAL5IpUyZZuXKlrFq1SsqXL2/ayhcvXmxmK1da7S5RooR5rGGc0O1c9BAAAAAAgAcrVKiQREVF2e3TsL158+ZYj9cAbrF9+3an319KR6UbAAAAAAAnIXQDAAAAAOAktJcDAOAi+W5ddPUtwE3wZyH5nU6fw9W3ACCFIHTDJXStwN69ezvkWhEREQ65DgAAAAA4GqEbLhEaGuqwazkqvAMAAACAozGmGwAAAAAAJyF0AwAAAADgJIRuAAAAAPBA06dPl3r16j3SucePHxcfHx+H3xNiInQDAAAAAOAkhG4AAAAAAJyE0A0AAAAAbuTOnTsSGRlpt+m+2Ny9e1defvllyZQpkzRp0uSxl9MNCwuT2rVrS5YsWcz1Tp8+bfaXLl1a1q5dK4sWLZJUqVLJrVu35L333pN33333sd4vJSB0AwAAAICbLa+rodd2i2vJ3a1bt0pgYKDs2bPHhOHu3bs/8vtev35dGjZsKM8995y5Xv78+aVly5by8OFDqVixohw6dEgOHDggtWrVkvDwcPO8UqVKj/FJUwbW6QYAAAAANxIcHCxBQUF2+3x9fWM99oknnpAPP/zQBO6QkBCpWbOmPHjwQFKnTp3k9126dKlkzpxZBgwYYJ6PHTtWcuXKJdu2bbOG7vPnz0uzZs3k4MGDhO5EotINAAAAAG5EA7a/v7/dFlfoLliwoAncqkCBAnL//n25cOHCI73vqVOnpHDhwtbnfn5+kjdvXjl58qQ1dGu7eZ06dWTfvn1y5swZKVu27CN+ypSD0A0AAAAAHkqDb1RUlHl89uxZU+HOkSPHI11LQ/uxY8esz3UcuV5Tg72Gbm0t11bz4sWLy/Lly6VYsWKSJg3N0wkhdAMAAACAh9Lq9MiRI8262wMHDpSmTZs+chDWtvFr166Z65w4cUJ69OhhAnbVqlVNkL99+7YEBASYlvPDhw/TWp5IhG4AAAAA8FAaiDdt2mQmU7tx44ZMmDDhka+lM6CvXLlSVq1aJeXLlzdt5YsXL7a2r2u1u0SJEuaxhnFCd+LQCwAAAAAAHqhjx45mexSFChWytqXb0rC9efPmWM/RAG6xffv2R3rflIjQDQAAAABeJGvWrLHuL1OmjGzZsiXZ7yelI3QDAAAAgBfZtWtXrPvTpUuX7PcCQjcAAAAAeBVtHYf7IHTD6wQHB0toaKirbwMAEnQ6/aMt6RKbfLcuOuxa8Fzu+mfKUffFn3MAnojZy+F1dCkDAAAAAHAHhG4AAAAAAJyE0A0AAAAAgJMQugEAAAAAcBJCNwAAAAB4gePHj4uPj4+rbwPRELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAN3Lnzh2JjIy023RfbH788UcpVqyY5MyZU2bOnGndHxYWJrVr15YsWbJIkyZN5PTp02Z/6dKlZe3atbJo0SJJlSqV3Lp1S9577z159913Zf369VKoUCFZsmSJFCxYULJnzy7jx49Pts/trQjdAAAAAOBGQkNDTVi23XRfdOfOnZM2bdpIv3795NdffzUBXF2/fl0aNmwozz33nOzZs0fy588vLVu2lIcPH0rFihXl0KFDcuDAAalVq5aEh4eb55UqVTLnXrx4UYYPHy7Lli2TQYMGSZ8+feT27dvJ/jvwJmlcfQMAAAAAgP8JDg6WoKAgu32+vr4xjluxYoUULlxY/vOf/5jnISEh0rRpU1m6dKlkzpxZBgwYYPaPHTtWcuXKJdu2bbOG7vPnz0uzZs3k4MGD1tB9+fJlE9gnTpwoZcuWleLFi5sKuIZ7rXzj0VDpBgAAAAA3ogHb39/fbostdP/1119SoEAB6/OiRYuan6dOnTJh3MLPz0/y5s0rJ0+etIZubTevU6eO7Nu3T86cOWNCtsqWLZsEBgaax+nSpTM/o6KinP6ZvRmhGwAAAAA8UO7cueXs2bPW5xqqlQbxY8eOWffreHA9TqvVGrq1tVxbzbWSvXz5cjMmPE2af5qgNeDDsQjdAAAAAOCBdNy2tofPmDFDjhw5YtrLlbaNX7t2TQYOHCgnTpyQHj16mIBdtWpVyZEjhxmjHRAQYFrODx8+bB3PDecgdAMAAACAB8qXL598++23JlzrTOVPP/202Z8pUyZZuXKlrFq1SsqXL28q4IsXLzazlSutdpcoUcI81jBO6HYuJlIDAAAAAA/VunVrs1mMGDHC/NSwvXnz5ljP0QBusX37duvjevXqyfHjx+2OZTz346PSDQAAAACAkxC6AQAAAABwEtrLAQAA4FT5bl109S0AgOeHbl3AXWfBw+OJiIhw9S0AAAAAANwtdGvgHjNmjKMul2L17t3b1bcAAAAAAHAQ2svh8fz8/Oy+rKBbAAAAAIC7IHTD44WGhto9p1sAAAAAgLtg9nIAAAAAAJyE0A0AAAAAHurXX3+VypUrS+bMmaVBgwZy5swZ6dixo/j4+MjevXvNMf/973/N8/Xr11vPmzp1qmTJkkXu379vd72vvvpKnnjiCUmbNq3UrVtX/vrrL+trGzZskNKlS0vOnDll9OjRDvsM06dPl3r16om3InQDAAAAgAe6efOmtGzZUrp37y779+83wfvdd9+1vq771L59+2Kcu2rVKomMjDSh3WLTpk3Sv39/+eabb+TYsWMSFRUlffv2Na+dP39eWrRoIa+++qps3bpVvv32W1m3bl2yfE5PR+gGAAAAAA904MABuXLlinTq1Eny588vH3/8sTx48MC8ljp1amvY1p/63OLhw4eyZs0aU13++eefrfsPHTokX375pamY58uXz1x3586d5jUN2U8++aQJ5cWLFzfvpdVyJIzQDQAAAABu5M6dO6YKbbvpvug0aKdKlUoGDx5s2sQrVqwoixcvNq9VqVLFhO1r167J3bt3TYi2+OOPP0zwfuONN0zF20JD9gsvvGB9Hh4ebgK22r17t9SvX9+0qatq1arJjh07EvV55syZY66j7ext27aVq1evJuq8+fPnS8mSJU07u1bzdZlqC12uWr8E0Op+mzZtrK9pdX7kyJFSsGBB0yY/duxYcTVCN7zyGz8AAADAk1fn0YBqu0VfsUflzp3btIKPGjVKihUrZh5bBAYGmtCtW/ny5e3O0+p29erVpVatWrJ9+3ZTLY/u0qVLpurdrVs381yDf+HCha2v+/v7y9mzZxP8LFu2bJEuXbrIp59+Krt27TJjxAcMGJDgeXpfHTp0kOHDh5u2999//13ef/9989rBgwflvffek7lz55ovEA4fPiwzZswwr+nvYOjQoSbof//99/LBBx+Y812JJcPgdfRbOwAAAMBTBQcHS1BQkN0+X1/fWI996aWX5LnnnjOV3zfffFP27Nlj9msVWMO0hlIN4Fq1ttDqtraWFylSxFSR165dK61atbK77jvvvGNC+fPPP2+ep0mTxu4e/Pz8zJjyxEyS1q5dOzMe3DJRW2LC+pQpU+T111+3Vt41tFs+p+U+tIKvXwT89ttv1vM0dOvvQe9dNW/eXJYsWSK1a9cWV6HSDQAAAABuREOlVpJtt9hCt4bXI0eOmEp4SEiILF++3ITTkydPmtfLlCkjP/zwgwndFjdu3DDVZ60gZ82aVS5cuGDXYq60aqyTpH399dfWfdmzZzeTqVlo23q6dOkS/CynTp0y4d5C28W1TT2p5xUtWlRu3bpl7kGDtlbh9cuJXLlyyb///W+5fPmyOe706dMybtw489l0W7RokfX34SqEbgAAAADwQN99951p3baoU6eOqUhr2FQatrWKbRu6ddkvnVRNK+La7j1o0CC7ydS0jVtnQNfW7YCAAOv+qlWrmlnLLXSCtbx58yZ4j/nz55fjx49bn2uYt1TP41OgQAE5evSo9bk+Tp8+vQnZ+mWDjlnXFnS9tn5x8Mknn5jjdOz6wIEDzWfTTceix9aan5wI3QAAAADggXSWca1a6/hlXZ9bq906eZi2fisN27retq6tbaFV7aefftqMAS9UqJBpK9dAqxXziIgI046t63prqL1+/brZlLaHb968WVavXi337t2TESNGSKNGjRK8x06dOpmW7x9//NEsQ6YBWAN1QvTLBJ0xXSvV2hqv7fZdu3Y1E7mFhYVJw4YNzf1oxV1Z1htv3769+cJA9+ukatpq/sUXX4grEboBAAAAwAPpBGnTpk0zE5Np27ZWkXX2ckvbd4UKFcx+2zZwrWo/++yz1uelSpUy4781jGt4//vvv82yYDoruGVTOvZbx1M3adLEVMA1CH/00UcJ3mPNmjVl8uTJ0rt3b6lUqZLkyZPHzC6eEA392uber18/8yVB5cqVrRVrDdwawF9++WUpUaKECdeWe9Hx4zqbedOmTc1s7tqKrtV8V/KJ0jt0AP0l6j8EPB5+j49P/xKaySFCxrv6VgCnaxhYQrxZ48CSrr4Fj5Hv1kVX34JHOJ0+h3jzPz9Hfj5v/zPljn8W3PF37o6/p5SgUrGC4q60Wq2zhz/zzDOSKVMmV9+OR6DSDQAAAABIFK0c65hsS+DW5bgsk5ZF37RNHSwZBgAAAAB4RNoGrhOWxUZnXQehGwAAOAmtznD0nwX+HADuRydt0wnZEDfaywEAAAAAcBJCNwAAAAAATkLoBgAAAADASQjdAAAAAAA4CaEbXidXrlyuvgUAAADA6aZPny716tVL0jnr169PcOKz48ePi4+Pj1PvIyUhdMPr5M6d29W3AAAAAHisAgUKyOXLl119G16D0A0AAAAAsEqVKpVkzZrV1bfhNQjdAAAAAOBG7ty5I5GRkXab7ovN3bt35eWXX5ZMmTJJkyZNJCIiIlHvsWTJEilYsKBkz55dxo8fn6j28j179ki5cuXMOb1795ZSpUrJuHHjrK9PnjxZAgICzLZgwYIkf25vRegGAAAAADcSGhoqWbJksdt0X2y2bt0qgYGBJhBrhbp79+4JXv/ixYsyfPhwWbZsmQwaNEj69Okjt2/fTvC8bt26yauvvirr1q2TqVOnmq1du3bmtbCwMBO0N2/eLJ06dZJevXo9wif3ToRuAAAAAHAjwcHBcvXqVbtN98XmiSeekA8//FCKFCkiISEhsnDhQnnw4EG8179+/bpMnDhRypYtK2+++aaplp87dy7B+9q1a5epqleoUMGce/LkScmWLZt57caNGzJjxgwpVqyYdO7cWU6dOvWIn977pHH1DQAAAAAA/sfX19dsiaEt4lrhtkyAdv/+fblw4YJp8Y6LBmWtjqt06dKZn1FRUQm+lwZqraznzJlTDh06JGXKlLG+Vrp0aeuExpZr4h9UugEAAADAQ505c8YamM+ePSupU6eWHDlyxHuOv79/kt9H30NDdo8ePSRPnjzSvn17U/F+nGumFFS6AQAAAMBDaRv3yJEj5ZVXXpGBAwdK06ZNJU0ax8e88PBw2bhxoxmzrWPM8+fP7/D38FZUugEAAADAQ1WtWlU2bdpk2sV1XPWECROc8j4lSpQw7eN169aVwoULS/r06c3EakgYlW4AAAAA8EAdO3Y0W1LUq1fPLAlmK/p47kKFCsXYN23aNBO6damxDBkymNnSdYmyESNGxLiP2M5PyQjdAAAAAOBFsmbNGut+HZO9ZcuWR7pm/fr1Zfbs2Wad7lu3bpkJ3IYNG8ZY7kQgdAMAAACAF9GlvWLzOLOK65Jka9aseYy7SrkI3QAAAADgRbS9G+6DidTgdfz8/Fx9CwAAAABgELrhdUJDQ119CwAAAABgELoBAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAHAjd+7ckcjISLtN90W3fv16KVSokN0+Hx8fmT59ulSrVk1atmwpWbJkkcaNG8tff/2VjJ8AtgjdAAAAAOBGQkNDTVi23XRfUmzfvl1q1qwpu3btEl9fX+nWrZvT7hfxS5PA6wAAAACAZBQcHCxBQUF2+zQ4J0W+fPmkX79+pvIdEhIiVatWlfv370uaNETA5MZvHAAAAADciAbspIZsdfPmTbvQrYFb5c2bVx48eCAXL16UgIAAh94rEkZ7OQAAAAB4IA3VDx8+tD7fsWOH9fHJkyclKirKPD516pSpcOfMmdMl95nSEboBAAAAwANpBVsnSDtx4oSpcg8YMMD62tmzZ8048GPHjsnAgQPNpGqpU6d26f2mVIRuAAAAAPBAxYoVk549e8rTTz8ttWvXNo8tatSoIdu2bZNy5crJ3bt3Zfz48S6915SMMd0AAAAA4KFGjRplNgttKdclw3RM+KJFi1x6b/gHlW4AAAAAAJyE0A0AAAAAXqRjx46yfv16V98G/h/t5fBaDQNLuPoWAKdbtedPh12LvzNwZ6fT5xBv5o6fj3tKfu74+fLduijuaMqhCw65TuPAkg65DhAfKt0AAAAAADgJoRsAAAAAACchdAMAAAAA4CSEbgAAAADwMnPnzpWpU6fKhQsX5Kuvvor32MuXL8vMmTPl/v37CV734cOH8v7778uff/5vXpmbN2+a/RYPHjyQW7duxXp+VFSUjBgxIsH30uP27dsnwcHBsmTJEnlc169fl3HjxokrMJEaAAAAAHiZ8PBwOX78uDRu3FjGjBljAuzYsWNjPVaDuc54njFjRmndunW8102VKpVZ/7tkyZJSosQ/k7A+9dRTcvv2bRPe06ZNK5kyZZLKlSvLwoULY5z/ySefyIkTJyRNmn+i6M6dO02Av3Tpkpw7d05Onjxp7nvv3r2SI0cOKVWqlGTPnj3We7lz546kS5dOfHx8Yg3tGuz1flT69Onlxx9/lGzZskm7du0kORG6AQAAAMDLaMjU4Jk3b15ZvXq1DBkyRG7cuGGCdWRkpPj7+1uPLV68uLz++usm+NrS0KoV68yZM5sA3b9/f7P/zJkzptr96aefSt26da1V7xdffFHatm0rbdq0ifOLgO+//1527Nhh3RcRESHTp0+XQoUKSYECBaRmzZoybdo0875+fn7xfsbAwED5+++/4wzdjRo1knnz5pnnqVOnljlz5pjrN23a1ITv5ELoBgAAAAAPdfHiRQkICJCsWbPGqALfvXvXVHe13Vsfa1hevHixqSBrNVqDqK3ly5ebdm7bluwqVarIpk2brI87dOhgrqWBdvPmzTJhwgTr8YcPHzbHxGXIkCGm0q3VaQu9jm4WV65ckTfffNNaCY+Phvik0Ip5t27dTJv5xx9/LMmFMd0AAAAA4KG0yquhWlvEbTet8NauXds81rbva9eumcCt7t27Z/atWLFCatWqZcKr5bxvvvnGVIMPHjxoWsbXr19vzqlTp44Jw127djWt3+rpp5+Wb7/9Vt59912pV6+euc4bb7xhHmsAt3Xv3j3ZuHGjqTI/Lq1i65cK+jOh4/QLAluvvfaa/PDDD5KcCN0AAAAA4EY0UGoLuO2m+2ITW2u10rZybb1Wu3fvNgHZtn1cW7x1vLdWvbNkyWLdX7FiRRO2dZy2Bm5LxblgwYLWtm+dpK1IkSJSpkwZ04KuAb1q1aqm6q3jpbWiHj3snjx50pxjGWOt9MuChIKzhR6nxysd+62t5zq+XD9/XJu+rl8W2NKuAJ34LTGTxjkK7eUpmLaO6F8ob6STRQAAAACeKDQ0VAYOHGi3b8CAARISEhLnObbt2Hrc22+/bYKuziqu1WgNu5YJySZNmiR9+/aVPn36mAnRNIBazj9//ryULVtWKlWqJM2aNTMTsGng1kC9a9cuadmypZQuXdoE8PHjx5vztE1dK9g67vuFF14wwVYDr62IiAiz31b37t3l66+/NuPPLSwhPGfOnHbHatjv3LmzTJw4UXLnzm3u09fX14RrrcxrtmnRooXduHLboG5Lr61V/Tx58khyIHSnYBq4CacAAACAe9EAGRQUZLdPA2ZstAKugdpSWdZgquFatwwZMsjWrVtlypQpsnTpUus5Gpp1GTENp+XKlTMVcA3AlsCuQfq7774zbeP58uUz+7WtvH379rJu3Tp56aWXzJjw7du3m3BuoWO8mzdvHut9ZsyY0YwLt6UBWjdbOqZbJznTUBzXuG4N9LahXAO57Th1/V3p47jofcT3uqMRugEAAADAjWhojCtkR6djszXQWujYbQ2gSsdrawjXFmttL7fQGcctdGKzt956y4x1/vnnn01I37Nnj3nNEriVtpCPHj1awsLCZNu2bSbQa6Vcl/+yVKevXr1qlviKTf78+eXo0aPiaDoju95DsWLFEnW8VvX1d0ToBgAAAAAk6NChQ/Lkk0/azWZuaSPXcdY//fSTCdNx0XW5dRkvbc3ev3+/me08emu30snTjh07ZoKzTpamYVeDq7aU169f34R/HSttCeo6cZqtbNmymaXHjhw5IkWLFnXY51+yZIkJ9Im95oYNG8wEc8mJidQAAAAAwEPpcl7aIq7VaV0KS1u+NYTqTx1KqkuJ7dy50xyr4fj48eN252s41qC8ZcsWyZUrlzWwR6et6D169DBjun///XczplsnM9N27rFjx5q28y5dusjQoUPlgw8+iLXy3LVrVxk8eLDDPrsGfx3rbmmNj83atWvtPrMuW6bLhiUnQjcAAAAAeCAdzz1nzhwThHXCMK36auDV9m+dmVzHbWs41hbzWbNmmdnILWtza4V81KhRUqJECdMyrs918rHKlSubcdk6Dlwr37azpmsb+YIFC0zIf/31161t5fp+Oht6v379TLVb30/Pja5Dhw7y559/2o0vj85SIY9tAjRbOpGa3qd+SaBjzy3082krvGUSNR0f/8UXX5jXdDI4nTyNSjcAAAAAIEHLly83Ld46sZku9aVt5DpL+KBBg2TZsmVmJm+dVVzHbOskaOXLl5d06dKZirZOpqat1lOnTjXrdWt7tp6noTgwMNBUrHUW8969e9tNxNyqVSsT0nVGdJ3A7JVXXjEt6drGruO8tZKt63lrpT06Hx8fs374sGHD4lyyyxLyoy85Zkur8nr/ej/63rbLkOmXDSNGjDCTrelEbDppmlbodbz57NmzzRcRyc0nKrELoyVA/2EwE7Zn/R69/Z/Z0AWrXH0LgNOt2vOnw67VMLCEuJvGgSVdfQseI9+ti+JuTqf/ZyIfAJ7LHf/doqYcuuDV/52pVKxgoo/V4Gu75JaO6Y5t9m6dbEwnXNPx2toKXqFChThbyS10DLe/v791YrbYaJi1Xec7MR4+fBhjSbGk0i8KGjRoEOcM585630fBRGoAAAAA4KFsA7eKKyDrWtsWOrY7MQoXLpzgMUkN3MoRwVcr2knlisCtCN0A4MEcWZ12ZNXcUdy1AgHPrbZRfYc7c9eqsjty1H8fVuwJF0+vdMP9MaYbAAAAAAAnIXQDAAAAAOAkhG4AAAAA8HDXrl2TS5cu2e3bvXu3WVorITpTuM5anljR30fP11nCEyMqKsrMLh7X7OXOoven65i7AqEbAAAAADzcZ599Ztattl1qS9fsHjhwYILn6lJjunb1mTNnEjxWl+nS5cUWL15s3ffLL7+YZch0JvWEfPLJJxIeHm5mHdfZxC3rcidEP1f0Y3V5sbgW49L9tsfrhHO6vJiuV57cmEjNzfj5+dmthedMuoYfAAAAAM/Xr18/s1b2/Pnz5bXXXjMV7s2bN8v06dPtjtNgrJuubW1Z3/pf//qXOUfXv9bgbgmtlqCry4xZLFmyRHLnzm2WJsufP7+kTp3aBHE9TtfOVlrF1sD/9ttv2723hu3vv/9eduzYYZ7v3LlTnnnmGXMfuoa3hugHDx6Y9b5tWe4lKChIhgwZYt2v64n//fff5tzo9JxGjRqZdcGV3uecOXOkZs2aZu3ybNmySXIhdLuZ0NDQZHuv5Ar3AAAAAJznyy+/NK3T2mI+dOhQs2k7tQZhy/Jg27ZtM2F25syZJgxr0LVd41qD+KRJk6z7LJViDdAaeHW/7hs8eLC8++670r59e7Ppfq0ejx8/Xn799Vdzrp4XWxAeMmSIqXSnS5fOPK9cubLcvHnT+rre94EDB+Sbb75J1OfWEJ8Uui55t27dzO/q448/luRCezkAAAAAeLArV65Is2bN5MSJExIWFma248ePy40bN+T333+Xffv2WcN0165dTTVZq9MazC2bVoA1dFue67katrUF3HLu119/LXv37pU8efKYfQMGDJAqVarIhx9+aMKyPtZNW89tA70liG/cuNFUmeOiAVzb1BOi4T++1nLb42zb7ZVW9H/44QdJToRuAAAAAHAjGigjIyPtNt0XF0ubeHwSc0x8NMT36dNHAgICrPtOnTolXbp0MWH/6tWrJuBr6Nb7je7kyZNSpEiReO9Dx5Tr2HQN7LZbvXr17I47d+6cGZabKlUqU1GPa9PX69SpY3eu3r+G++ScyI32cnik4OBg8+1cXMaMGZOs9wMAAAA4cshp9AnQtKocEhIS6/GJmYxMQ6Zt4NUWb61iW2j1e+vWrSZEK33tvffek+HDh5vnixYtMtV0rapbaKjVNu1Ro0ZZ9+lY8ho1asQ6n1SATWCPjc6gruPSn3/+eeu+sWPHypo1a+yO0zHl+j6+vr4mXGuVXvNBixYt5MUXX5S2bdtKmzZtTKVbP1d0Okb9woULpmKfHAjd8EgauAnWAAAA8EYaIHXSMFsaMOOi1eApU6aYLTY5cuQwbda2oVsrxTrr+FNPPWWe6+zlGrg7duxonrdr18469lr17NnTVIg1zNoaNGiQGSdtYfvYVsaMGeNdVkzb2f/44w+pVauW3X6tmutYbFsa9m0nd9Px6PoZM2XKZCZM09+VPo6L3kd8rzsa7eUAAAAA4EY0NPr7+9ttcYVubTvXsHz27FnZtGmTafl+5513zKzeWs3VTWcxjz4juAbXhGiAtdCKsgbn6HRitHLlylk3y2zh0eXPn1+OHj0a53t99913pkKeJUsWu/1aWY9vpnEN69reXqxYMUkMrfjrhHOEbgAAAABAglauXCmtWrUyYfvZZ58161H/9ddfdhVnrVLrjOUJTTz2KPr372+dvE23V155JdbjsmXLJpkzZ5YjR47EeE0r1Tpzuc6KHt3ly5djVLpt6RJmGuh17fDE2LBhg6nqJydCNwAAAAB4qOXLl0vjxo1N2La0g+swzAIFCpjxzFrV1bWxdTkvXcvbIqEArkHYttIdl8RWui0zp+uSY9FZJmh74YUXYrymFXvbVvLoVW4d6969e3eJy9q1a80kcLbLlsXVAu8shG4AAAAA8EAaOufOnWsq3Tpe27I8lraB6wRnGpo16Ooa2loRtp04LPrkazpxmgZxncxMq9I6oZlWzqPTIK/vYzk/rkp3bLOtd+jQwVx/6dKl1n06Ydzs2bNlxowZdi3vWsGfPn26mdxNw3x0OpFa8+bNTRXctkKun/ngwYPWSdR0fPwXX3xhXpswYYL5HSR3pZuJ1AAAAADAA2lI1UCprdUahDVsFyxY0LRxKw2dOmmYBlttwbadnG3atGlSqFAh63MN0XqNEiVKmBBctWrVWCvMekx4eLi5no4zX79+vQwbNszumGXLlpkJ1qKP4fbx8TGVcA3mOkP54cOHZeHChbJ69eoYY7J1tnRdgkwr2c8884zda1u2bDEzleu9/vjjj3YTxGnVXyvnWtFWZcqUkR49epglzfRzrVixQpKbT5SDGvt79+7NbNIexpP/mSXm3ocuWJVs9wN4g1V7/hR3M7pdc1ffgsfId+uiuJvT6XO43edz5D0BKeHvsbty1N/lFXvCxR190KqheLOHDx9aq9oaRzWMJ5WG5wYNGpiZ2x/lfZMTlW4AAAAAQLJJZRN8HyVwWyraj/O+yYkx3QAAAAAAOAmV7hTMz8/PtGl7ooiICFffAgAgAbTKAp6PIRnA4yN0p2ChoaHiqTz1ywIAAAAAKQvt5QAAAAAAOAmhGwAAAAA8WGwLUuns3n/88UeC5+oSYLGtqZ0Y165dkxMnTiT6+EuXLtk9v3Xrlty8eTNR5+o9jho1SpLb2bNnzRrij4PQDQAAAAAeau7cuVKnTh2z5nX0OZDefPNNs1Z3dCNHjpSJEyeax9OnTzdrZOua3ZYtX758ZlZxXU87tvMtvvnmG3nuuefMWuAJOXPmjOTJk0d27txp3fftt9+adbQTs4r1W2+9ZQ3oek/3799P8By9rn6pEP3Y+L5k0GXFdM1yiyxZssjnn39u1iN/VIzpBgAAAAAP9cILL8gvv/wi//rXv6RNmzYyadIkyZo1qwmakZGRUrhwYbNUloZP3b9//36pVKmStGjRwoR1Dea67dmzR7744gsTyDt27GhC9+zZsyV16tTmfW7cuGGukTZtWuva2J07d5ZNmzbJjh07pHr16nahVYNx9uzZ7QK2vt+yZcukZcuW5p40rOum96j0vC+//FKaNWtm9xnXrl1rKupff/21eb548WL597//be5D71Mr5vpTJ4qOLXRraP7Pf/5jF6SjH2uh96+/D0tVPWPGjOaLjdatW5vPqZ8/qQjdAAAAAOChNDxOmDBBjh07Jnnz5pVhw4bJli1bpH379nL06FHzugZgS3hWGtD79etnd53ixYvLyZMnpWDBgib0agXc9pzBgwebQK5B1xK6lVafFy5caD1Wg66GZ61q6/UsgX306NEyZcoUadSokbz//vvmeL1mWFiYCbVKA7Lte1qEhISY4GzRqlUrs1loSA4ICJBPPvlEEuP27duSFPq7qV+/vsyZM8f8XpOK9nIAAAAA8FDz5883oVqrxenSpTOhV1f60aCqgVvD7jvvvBPjvI8//ljKli0rgwYNMgG5atWqJqRriNYW8AoVKpgq7/bt260rH2n1XAOrpUKt25NPPinLly+3PrdUxC2BW+kXAefOnTPvo5XiLl26SJUqVWT8+PGyZs0a81i333//PUbo1vOuXLkiTz31lMRF7zlXrlySEK1ia2t5Qu3s0VvMVbt27eT777+XR0HoBgAAAAA3osFQW8Ntt9jGIV+4cEG6d+9uqrCnT582+9577z0TmLUiq+G3adOmphL96aefmte1Ct61a1cJCgoyz7Vq3atXL1NxXrlypeTPn9881k2DuAb5x7F9+3YT/LNly2bdd+TIEVPl/vvvv+X8+fMmbOfMmTPWCrTlPhIaL65fNFiq8JZN2+Rtbdu2zXwRoa3t2o4e16bBX9vXbemXAtqa/yhoL4dH0r8s8a3VPWbMmGS9HwAAAMBRtKo8cOBAu30DBgww1WtbGlTDw8PNeGVtfa5YsaL5/+DcuXNLiRIlzBjuHDlymJZuvWZgYKCpiNeqVcv8v7SGYQ2n+pqOBddKtlaWdTI1paHYtvKsM3lrC7vtPq2ya7u6hlXLc21314nPLGO5e/fuLYsWLbKeo6FXx4NnyJDBuu+vv/4ybefR6YRw2joenz///NOE89KlS1v36XtGnwSucuXKJuT7+vqa+7VU6fVLCn1NK+81a9Y0lW7L57HQ5/FNKhcfQjc8kv6LAQAAAPBGwcHB1kq0hQbF2OikYPPmzTMTjel4bG3X1qCqFWvd16BBA5k8ebL897//NbOSaxDXUG0Z063t3vpYA+/x48fN8ZaZ0OvVq2f3XpbJx7TCrtdRes1Zs2ZZj61du7ZddXz48OEmZNuGbqWTojVu3Nj63PaxLW1xj2929EOHDpmKfqlSpez2a3eA/g5s6WfVLyqUBmutrOvzTJkymVCdPn168zgu0YN4YhG6AQAAAMCNaMCOK2TH5rfffjNt5NpmrS3QGzduNCH4119/NbOKa+j+8MMPYz1Xq7cajOOqdNvS8JwYtpVw3zg+R7du3ewCblzrfesXCdqOHt+yZTrxW/RArOPAtbIfX1hXBQoUkMTQLzJsW+STgjHdAAAAAODBdFbxN954wwRcrd5qC7jt+GTb2caj06CtlW6tcuvyXxpy9bFu5cqVe+TqbkImTZpkHTuu29NPPx3rcRqcDx48GGu1W4OwtoS/++67MV67fPmy3ZJl0S1YsMBU5bW6nRhLly6VJk2ayKOg0g0AAAAAHkoD66pVq6zVYJ247Pnnn491UjKd5dsyjlrXttbQqrN0x7b2dN26dWXv3r3WdmyV0KzfluvGtuzXo1a6NfTrpGY6EZyOa7e9Fx0XruPVdd3x6LRKb3vvtnT8uF7vq6++krj89NNPZvI0HU+uv6OxY8eaCekeBZVuAAAAAPBA2hqu4VXDpy6ZpYF7xYoVptVcxyxrMNXQapn5/OWXX5YlS5ZIjx49zARrOnmYVrf1GG1L1+N0bLfOhK4znM+ePVt2795tlgFT0ZfRUpb3Wb16tRlfruFfJyOL7V7v3LljKutxVbr1OrHN0q7j0XXcus5yrvQaOnncnj175IsvvrAep/t//PFHM5Gbto/rkmjR6XrmGtT1SwXbtb71i4IDBw5YvzjQ36sux6a0NV9b2IsWLfoI/5SodAMAAACAR9IwrBOIaVDWcdwtW7aUcePGmQCu4VWr3fpYQ7UGYh2TrMFSw6qGUq18a7D8+eefZe7cuabyrYFTJ3HTMK200muZWE0r0zpLuk5uZqFBXCcy02XL9Brasp05c+YY93r37l0TzDX4a2VdQ210HTp0MOPJf/nlF7v9Wp3Xa2v41tnGdQy7BvD169fbtZBrG/2XX35pWsunTZsWY7z2Dz/8YN5DJ4vTyd9saeu4zrj+5ptvmuf6xYEuu6a/J/19aZh/VD5RiekRSASdkp1lmuBOhi5Y5epbADzKqj1/irsZ3a65q2/BY+S7ddHVt+ARTqfP4epbADzq77G3/51ZsSdc3NEHrRom+Ryt8u7cuTPBNa3V1atXTQj3NA8fPrRO5map4if1fF2LXNvvH/V9HwXt5QAAAADg4bTKm5jArTwxcCvb4PsoE7zp+UkN3NHf91HQXg6v1TiwpKtvAfAo7vh3JmjWUoddi6p54nh7VQtIKX//HFU1d8fquyN1KR77RFuAI1HpBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAgJfQWb11ze2k0CW2dNkwyzJhiaXH6xrbiB+hGwAAAAC8xO7du6VWrVqya9euOI+5cOGC3fMRI0bIf/7zH7l27VqS3isyMlKqVKki27dvf+T7TQmYvRwAAAAAPNDdu3dNUE6XLp2kTZvW7CtVqpS8//77snHjRvPYUv2+d++eOf7IkSPSuHFj+emnn0w437p1q3z66afy5JNPSoUKFeTmzZsmTG/evFkqV65s9376+o0bN8ySY7pk1507dyRfvnzSuXNn8fX1Ncfcvn3bhPrp06eb9wGhGwAAAAA80rp166RJkyZmjW5L6FYahjUUBwcHW0P3/fv3TfD+888/5bPPPpOWLVvK8uXLpXXr1jJ69Gjp3r27OU6v16BBgxiBW2mQ12Ms63y/8sorUrp0aenfv7+5B8t7a8t5bOenVIRuAAAAAHAjGlx1s6WVZEs12aJRo0by4MGDGOe3a9dOihUrJiEhIbFeX197/vnnpUePHlK2bFn58MMPZfjw4aaKrdVwHROuQVwr5WvWrLGeN2rUKFm8eLGsWLFCDh06JAcOHJCJEyea869cuSLjx4+X9u3by+nTp+WXX36R1KlTO+x34skI3R5Cv6XSVg0kzpgxY1x9CwAAAMAjCQ0NlYEDB9rtGzBgQJwh+lHkzp1bZs6caSrkWhVX5cqVM8G5Xr16Mn/+fBk7dqzdOYMGDTLH/vrrr9KxY0dp2LChuU8N2D/++KMJ4/plgQZ1Avf/ELo9hAZugiQAAACQMgpuQUFBdvuiV7ltlShRwozVtoRnyyzkgwcPtj5/+eWX5bvvvotxroZkvbblXFsanHWMt5+fn6RK9c8c3D179jRjtr/66itT6V67dq1MmzbNBO9mzZrJ2bNnra3m+AezlwMAAACAG9EQ7O/vb7fFF7o1FP/www9m3LZur732mnz88cfW5x988IGZbM2WVrGHDh1qWs0LFy4shQoVMuO927Ztax6//fbbpqJdtGhROXr0qAnn48aNM2O1NWjr9s0335hx3bNnz5Z///vfsnTpUjPOXGc01y8B8A++ggAAAAAAD2apQsfHtt1bK9Q6Djs8PFz++9//WivZ0dvL9fH69eutnbcLFy40Y7qLFy8uW7Zskb59+5rt+++/l4IFC8qJEydk7ty5ZtNZzfEPQjcAAAAApCAfffSRvPPOO5IzZ04pUKCAqaJr6NbWcK10awhXtjOQa/u5znauFXN9rOPBLa3w2t6us55bvPTSS9a2dRC6AQAAAMCj6TJe8bl165a10r1q1SrTGj558mQTis+dO2c9zrbSHdvkblrBjj7+W9cJb9GihV37uo4h1+e///67gz6hZyN0AwAAAIAH0/W3bWno1SCuy3jpOto68dmsWbPMazo+XAN09uzZY1xHx3/HRWdOj2329KxZs8qSJUvM2HDEjtANAAAAAB5sxIgRUrFiRbsQruttayDWSc60/Ttv3rzmtRo1apjNlo7JHjZsmBw+fFgCAgIee01x2CN0AwAAAIAH06W6oodoi/r16yd4fq1atcxs5bVr15aSJUsm6b21dR3xI3QDAAAAQAqmVfA33njD1bfhtQjdAAC3Nbpdc4ddK2jWUodcp2FgCXGUxoFJqybAe+W7ddEh1zmdPodDrgOkFI78O+Oov8fwPgkv6AYAAAAAAB4JoRsAAAAAACchdAMAAAAA4CSM6XZzwcHBcvv2bYmIiHD1rQAAAABwc7o+94EDB6RMmTJOfy9dD3z//v1Srlw5p7+XJ6PS7eY0cI8ZM0Zy587t6lsBAAAA4OZ2795tlgDbtWtXvMeNGjVKevbsaX1+6dIl8/P06dOSLVs263Jg8S0JFhkZKVWqVJHt27c77P69EaEbAAAAADzQ3bt35eLFi3Lt2jVTrNOtVKlS8v7778vGjRut+zQ4a0C+cOGCPHjwQG7cuCEjRoyQSpUqmWvoVqdOHVmyZIn4+vqaTU2ePFkaNWpkfb8KFSpIsWLFpHLlyiZsP/PMM5IvXz7p3Lmzea6bVr3z5MkjK1ascOFvxr3QXg4AAAAAHmjdunXSpEkTSZMmjaRNm9a6/86dO+Lj42OGqlpazu/fvy/37t2TP//8UwYNGiTXr1+XXr16Sffu3WXGjBkmhL/zzjuyZcsWc86VK1dk+PDhMnv2bOt1NcjrtbJkyWKev/LKK1K6dGnp37+/uQfLe4eFhZlgjn8QugEAAADAjWhw1c2WbQXaQqvQWrmOrl27dqYiHRISEuO1H374QTZs2GDCt1apLbTaPXfuXFMV12CtzydNmiTVq1e3a0lfvHixqWIfOnTIjB2fOHGifPjhhyakjx8/Xtq3b29a1H/55RdJnTq1g34jno3Qjcee5M0d6Th4AAAAwBOFhobKwIED7fYNGDAg1hCdVK1bt5aDBw9KjRo1JFWqVCZcnzlzxoTxoKAg00JesWJFE541mGs1e9iwYeZcrZBrBf3XX3+Vjh07SsOGDc19asD+8ccfTRjXLwvWrFlD4LZB6MZjT/IGAAAAwLHFLQ3AtqJXuW2VKFFCjhw5YgKxZVZxNXjwYOvzl19+Wb777jvz/L///a8Zd71t2zbz//MakPX6devWNdVuCx0bHv19dfI1HRv+1VdfmUr32rVrZdq0aSZ4N2vWTM6ePWttNcc/mEgNAAAAANyIBl1/f3+7Lb7Q7efnZyrVOm5bt9dee00+/vhj6/MPPvhA0qVLZz1+4cKF8scff8ixY8dMu/iOHTtM1Vsr1ToRmmWbOnWqNchrBXvcuHFmrLYGbd2++eYbUwnXcd///ve/ZenSpWacuU6opl8C4B98BQEAAAAAHkwDc0Js2701bGu1Wmc913CcPXt2s79x48YxKt0WOs5bw7qG9OLFi5sJ1/r27Wu277//XgoWLCgnTpww5+tmO148pSN0AwAAAEAK0q9fP5k+fboZm62hWSvlixYtsla6Lc6dO2dmNFda8V6+fLmpmOvjmTNnWlvhtb1dZ1G3eOmll0xlPL7qfEpC6AYAAAAAD6ZV6PjojOSxTWz2999/S61atWTevHmxVro1nNtO7qavaZC2tJwrrZa3aNHCrn1dx5Dr899///2xP5s3IHQDAAAAgAfT9bdtaejVIK7LeOk62tpKPmvWLLtjtBVcW8V1RvSiRYvK3r17YwR1XfpLq9hKj4tt9vSsWbPKkiVLzBJliB2hGwAAAAA82IgRI8wyX7YhXJcC00Csk5xpcM6bN6/dOTlz5jTralvaxzVk29Ilw1auXCnz589P8prisEfoBgAAAAAPpkt12dKJzSzq168f6zmWsG2hM57rZqEzl2fIkCHB5b+ih3XEROj2EDq5Qe/evcWdREREuPoWACDRGgb+0x73uFbt+VMcpUvxnA67FuDO8t266JDrnE6fQ7z5ngB3osuUwTEI3R5CJy5wN+72JQAAAAAAuJuEF3QDAAAAAACPhNANAAAAAICTELoBAAAAwEvoUmH79+9PlvfSpcnCwsKSfH8jRoyQ+/fvO+WeRo8e7XazqRO6AQAAAMBL7N69W2rVqiW7du2K97hRo0ZJz549rc8vXbpkfura3NmyZbPOTB7f7OSRkZFSpUoV2b59e6Lv75NPPpHw8PAEZ0V/VDdu3IgxM7urEboBAAAAwAPpWtwXL16Ua9euye3bt81WqlQpef/992Xjxo3WfRqcNSBfuHBBHjx4YIKpVpsrVapkrqFbnTp1ZMmSJeLr62s2NXnyZGnUqJH1/SpUqCDFihWTypUrm7D9zDPPSL58+aRz587muW7lypWTPHnyyIoVK2Lcb3h4uFnObOLEieZ5vXr1xMfHx24rVKhQgq/pT11n3KJatWrmddW/f385duyY/PLLL+IumL0cAAAAADzQunXrpEmTJqZqnDZtWut+ba/WEBocHGxt6dZ27nv37smff/4pgwYNkuvXr0uvXr2ke/fuMmPGDBPCtUK8ZcsWc86VK1dk+PDhMnv2bOt1NcjrtbJkyWKev/LKK1K6dGkTdC2Va31vbTnXYB7dkCFDTKU7Xbp05vmPP/5o7kvv8+rVqzJhwgRJlSpVgq8p/bLh3Llzkjt37hjt9FrF79Onj6xdu1bcAaEbAAAAADyQVqG1ch1du3btTEU6JCQkxms//PCDbNiwwYRvrVJbaLV77ty5piquwVqfT5o0SapXr24XZhcvXmyq2IcOHZIDBw6YqvWHH35oQvr48eOlffv2pkVdK82pU6e2nquBX0P71KlTrfsyZcpkfmplXYN41qxZE/Wa0mvv27dPbt68aar5tipWrGiq+hERESaUuxqhGwAAAADciFaLo08GZtv2/That24tBw8elBo1apjKsYbrM2fOmDAeFBRkWsg1tGp41mCu1exhw4aZc7VCrhX0X3/9VTp27CgNGzaUgQMHmoCtlWkN43rfa9assQvc6uTJk1KkSBG7ivzj0FZ2S+iuWrWquSdbuk8r7s8++6y4GqEbj8zPz0969+4t7mjMmDGuvgUAAADgkYSGhpowa2vAgAGxVq5ViRIl5MiRI9ZxzTqruBo8eLD1+csvvyzfffedef7f//7XjLvetm2b+f9mDcga6OvWrWuq3RY6Njx60NfJ17SK/NVXX5lKt7ZwT5s2zQTvZs2aydmzZ2OdJC0iIkICAgIe+3djERgYaEK3jk/Xx9FDt1a49T3dAaEbj/UvAwAAAACOpeOYtepsK74qtxbDtFL9wgsvxNpe/tFHH8mJEyesxy9cuFD++OMPM+GYtotraNeqt1aqdSI0Cx0zbZkJXCvYGrR1Sa5+/frJzp07ZfXq1aYIp+O+df/MmTPNOHNtOdf7KVq0qPVaGTNmNOPIHUWDtn6JoKFbq/bR6XtZWtRdjdANAAAAAG4kqa3kthOMxcW23VvDtlarddZzrZBnz57d7G/cuHGMSreFjvPWsK4hvXjx4mbCtb59+5pNZyQvWLCgCfZ6vm6248VV/vz55ejRo+IoOkv74cOHTeh+8803Y7yun6tAgQLiDgjdAAAAAJCCaKV6+vTppiVbQ7NWyhctWhRvpVtb15cvX24mNdPHWtW2VOW1Uq6zqFu89NJLpjJu+8VBtmzZJHPmzCYM21bAH5W2sGu7+vHjx02gj17l1kBevnx5cQeEbgAAAADwYFqFjo/OSB59YjP1999/S61atWTevHmxVro1nNsOLdXXNEhbxo4rrZa3aNHCugyYZQy5Pv/999/t3q9r165mnLmOAXdUi7ll+TJbn376qZkIzvY+XYnQDQAAAAAeTJfjsqWhV4O4LuOl62hrK/msWbPsjtFWcG0V13HfWnneu3dvjKCuS39pFVvpcbFN5KZLeS1ZssSMIU9Ihw4dZPLkybJ06VJp3ry5PC6daT166N6xY4fMnz9ftm/fLu6C0O1E2moRfc24pHKXGfcAAAAAuKcRI0aYZb5sQ7guBaaBWCc50+CcN29eu3Ny5sxp1tW2tI9ryLalleKVK1eaAJvU5c3i4uPjY6rqugzZ888/b53l/LPPPovznNhe05ZyVa9evRjVfp3lfc6cOaZl3l0Qup1IA/fjLl3lrktyAQAAAHAPulSXLZ3YzKJ+/fqxnmMJ2xavvfaa2SymTp0qGTJkiHX5L1vRw3pC8ubNKxs3bkzU5G+PQqvuzrr2oyJ0AwAAAADs+Pv7O+3aqZwYit0tcCtCN+Ch8t266JDrnE6fQ9ztnhx9X4BqHFjSIdfpUjynOMorP2xx2LVGt3v8sXHu/PeYf+d5Nnf8fO54T+74+Rz559yRvP3zwbu439cAAAAAAAB4CUI3AAAAAABOQugGAAAAAMBJCN0AAAAAADgJoRsAAAAAvJCuZ61rY8O1CN0AAAAAADgJoRsAAAAAACchdAMAAACAG7lz545ERkbabbovNjt37pSaNWtKpkyZ5Omnn5Z9+/YleP2wsDCpXbu2ZMmSRZo0aSKnT5+2vrZ69WopU6aMZMiQQWrVqiWHDx+2vrZixQopX768ZM2aVbp06RLnPUU3f/58KVmypOTMmVO6d+8ut2/flpQkjatvAIkTHByc4v5wPo4xY8a4+hYAAACARxIaGioDBw602zdgwAAJCQmx23f16lVp3LixvPvuuybYjhw5Ul5//XXZtWtXnNe+fv26NGzYULp27SrffvutDB06VFq2bCnbt2+XVKlSSbt27aRPnz7Stm1bcw/9+/eXOXPmmPCtx33xxRdSt25dad26tXm/jz76yITp+/fvx3iv9evXy71796RDhw7mvUqVKiUdO3aU999/Xz777DNJKQjdHkIDN0ESAAAASBkFt6CgILt9vr6+MY776aefJFu2bCb4WoK5Vr3js3TpUsmcObM5Vo0dO1Zy5col27Ztkxo1akj69OlNUM6ePbt89dVX1jD93XffyVNPPWUq3Ortt9+Wr7/+2rz3jh07JCoqKsZ7Pfnkk+YLAf0i4IUXXjD7Pv30U3nuuedMtkkpk7wRugEAAADAjWjAji1kR3fq1CkpXLiw9bkG8DZt2iTpHD8/P8mbN6+cPHnShO5Zs2aZivqIESOkQoUKJhxXqlTJtKBrK7u2lisN49rSrgoWLBjv+9WpU8f6vGjRonLr1i05f/685M6dW1ICxnQDAAAAgAfKnz+/WRbMtnW8XLly8vfff8d5ToECBeTYsWPW5zou++zZsyY437x504Tpn3/+WS5cuGDGfWs7uMqXL580b97ctK7rtnv3bnNcQgoUKCBHjx61PtfHWk3X6npKQegGAAAAAA/UtGlTuXTpkhkDrpXowYMHy4MHDyQgICDOc5o1aybXrl0z47VPnDghPXr0kOLFi0vVqlVN4G7UqJEZfx0REWFaxi3t5TrGe+PGjXLo0CFThR83bpx06tQpwXvs0qWLud6iRYskPDzctM3rePKU0lquCN0AAAAA4IF09nGdUXzJkiVSunRp+fXXX2XhwoXxBlptCV+5cqWsWrXKzESubeWLFy82k6j5+/ub9nIN79oGruO/J02aZM7T5zNnzjShuVixYrJnzx4zwVpCqlSpIjNmzJB+/fqZ2dUrV65sviRISRjTDQAAAAAeqmLFirJ169ZYXytUqFCsE5xp2N68eXOs57z00ktmi43OlK5bUr0UzzVTAirdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkjOmG18p366Krb8EjuOvvyV3vC3Ck0e2aO+xaQbOWOuQ681rXEm/+e+yO9+Su93U6fQ7xZu74O3dHjvxz4MjfOf/84EmodAMAAAAA4CSEbgAAAAAAnITQDQAAAACAkxC6AQAAAABwEkI3AAAAAMAtbdmyRUqWLCmejNANAAAAAClMoUKFZP369eLOduzYIS+++KLcuXNHPBmhGwAAAADgVm7cuCGtWrWS7t27i6cjdLs5Pz8/6d27t0RERLj6VgAAAAAkA63sRkZG2m1xVXs7duwoISEhMmvWLNOGPXHiRLN/+/btUr16dcmSJYsJr1evXjX7GzduLD4+PnLixAmpX7++eTxs2DDz2vTp06VevXrWax8/fty8bqGv6TGjR4+WggULyrJly+zOmzx5sgQEBJhtwYIFifqsc+bMkcKFC0vGjBmlUaNGcuHCBbM/bdq0prX8mWeeEU+XxtU3gPiFhoaanxq8g4OD5fbt266+JY8wZswYV98CAAAA8MgZYODAgXb7BgwYYMJ1bFauXCkrVqyQUaNGSaVKleTKlSvy/PPPmyrxvHnz5I033pA+ffrIlClT5IcffpB79+5JYGCgTJgwQWrXri3p06dP9L19+eWXkjVrVvOzatWq1v1hYWEmaG/evNm8T69evUzYj8+1a9ekQ4cOMnPmTKlRo4a888478umnn5rPny5dOsmbN68cOnRIPB2h24No4CZMAgAAAN5Ni21BQUF2+3x9feM8/siRIyacalVbffvtt6ZSrEFdK9V9+/aV9u3bm9e0oqxSpUolmTJlMgE6Ka5fvy4bNmwwoTh6O/iMGTMkd+7c0rlzZxk+fHiC10qTJo3Z7t69K0888YQsWbJEHj58KN6G9nIAAAAAcCMasP39/e22+EK3VostgVudPn1azp8/L9myZTOh+pVXXjHPk9o1e/PmzRj7unXrFiNwq9KlS5vArWJ7PTZaYZ87d6589dVX5twWLVrIqVOnxNsQugEAAADAg1mq1xb58uWTypUry65du8y2e/du2blzp6l+W2ilOyoqyu48rYrbVpp19vCE3stCvxhIqkuXLpnx35s2bZJz585Jzpw5TVu6tyF0AwAAAIAXadq0qZw8eVK2bdtmqsnz5883E6jZhuyiRYvKqlWr5K+//pI1a9aYfTqGev/+/WbiNq2Mjxgxwqn3GRERYSZg0/HoGsDV/fv3xdsQugEAAADAi2hLuY6P1knJihQpIt9//715ruOnLUaOHCk//fSTFChQwDpB27PPPisNGzaU8uXLm+D+wQcfOPU+S5UqZe7xrbfeMl8ChIeHm/vyNj5R0XsKHpHOrs0kX877nei1FL/jxIvY+4erbwEA4nU6fQ6HXSto1lKHXGde61oOuQ48nyP/fLqjfLcuuvoWUtyfA37niZe7fCVX3wIciEo3AAAAAMDhtMU9a9assW5t27aVlIIlwwAAAAAADvfkk0+aidxikyFDBkkpCN0AALdFe2vyt4W/8sMWcZTR7ZqLu6G9NfH4XSU/d2zl5s8BHoeOIS9UqJCkdLSXAwAAAADgJIRuAAAAAACchNANAAAAAICTELoBAAAAwEvoitD79++P95grV67IzZs3H/u9Hj58KGFhYUm+vxEjRsj9+/fFGUaPHi137twRd0LoBgAAAAAvsXv3bqlVq1acs4arN998U4oVKyb58uWTtGnTmsnObDdd0uuDDz5I8L0iIyOlSpUqsn379kTf3yeffCLh4eFmkjVnuHHjhrzzzjviTgjdAAAAAOCB7t69KxcvXpRr167J7du3zVaqVCl5//33ZePGjdZ9t27dMgH5woUL8uDBA5k3b56cPXtW5s+fL9WqVZPjx4/bbbqGdmBgYIz3q1ChggnrlStXNmH7mWeeMcG9c+fO5rlu5cqVkzx58siKFStinB8eHi7ff/+9TJw40TyvV6+e+Pj42G2W2c7je01/5syZ03pd/Qz6uurfv78cO3ZMfvnlF3EXLBnmIfz8/Mzi8gAAAACg1q1bJ02aNDFVY61YW2h7tYbQ4OBga0u3tnPfu3dP/vzzTxOcLSFYK+K2y3qNHDlSDh06JIsWLTLhfdq0aVK/fn3zmgZ5vVaWLFnM81deeUVKly5tgq6lcq3vrS3nGsyjGzJkiKl0p0uXzjz/8ccfzX3pfV69elUmTJggqVKlSvA1pV82nDt3TnLnzh2jnX7UqFHSp08fWbt2rbgDKt0eIjQ01PyBAgAAAADVqFEjU7nWoHv9+nXr1qZNG9MebnmuLdd6jI7BtgRupcH6u+++M5Xh3377TS5dumQqzGvWrJG///5b0qdPL4ULF7YLs3Xq1JG//vrLVJIPHDggPXr0kA8//FC6du1qQn379u3NPr0vW/fu3TOhvWnTptZ9mTJlMq3svr6+JojrY39//wRfU6lTp5Z9+/aZyrxW821VrFjRVPUjIiLEHVDpBgAAAAA3ogE5+mRgGj51c5S9e/fK0aNH5cyZM6YlvXjx4vLyyy9Lrly5zOsamvW1AgUKWM8ZNGiQqaD/+uuv0rFjR2nYsKEMHDjQBHCtTGtLud63hnYNxbZOnjwpRYoUsavIPw5tZdfQrRPCVa1a1dyTLd2nFfdnn31WXI3Q7aG0zSL6Nzr4nzFjxrj6FgAAAIBH7nLVMGtrwIABEhISEuvxJUqUkCNHjljHNWtFWw0ePNj6XAO1VrXViRMnTGv4V199JbVr1zaTr+k465o1a5r2c72eVr/z5s1r19KtevbsaarIeq5WurWFW1vQNXg3a9bMjBWPbZK0iIgICQgIcNBvSMyYcw3dWsXXx9FDt3YJU+nGY9HATbAEAAAAvLPAFhQUZLcvviq3zv/0ww8/yAsvvGCet2vXzrSRW0L6Rx99ZIK20rbx6tWry7Bhw0x1++233zaVaR3bvWTJEmnQoIFMmjTJhHydBd1CK9gatHVJrn79+snOnTtl9erV0rt3b5k9e7bZP3PmTDPOXAO83k/RokWt52fMmNG0ujuKBm39EkFDd40aNWK8ru+lLerugNANAAAAAG4kqa3k0avRsbG0e+vM4nv27DGVYG0h1zZtHautoVhnJ9fx2Dr+WidRsx1/rROoLVy4UBYvXmzC+pYtW6Rv375m0xnJCxYsaIL93LlzzaazmtvKnz+/aWd3FG2JP3z4sAndugRadFr5t22NdyVCNwAAAACkIBq4s2fPLhkyZDDPNbRqKNalxXSmcA3mly9fNpOjjRgxwhyjrevLly83k5rpY61qW6ry2o6us6hbvPTSS6YybvvFQbZs2SRz5swmDNtWwB+VtrBru7pOpKb3Hr3KrYG8fPny4g4I3QAAAADgwbQKHR8N09EnNtPwvGnTJvNY27M1vOpEaNp2vn79elPp1rZ123HmWsHWIG0ZO650jfAWLVpYlwGzjCHX57///rvde2qI13HmOgbcUS3mluXLbH366admFnXb+3QlQjcAAAAAeDBtB7eloVeD+JUrV8w62jrx2axZsx7rPXR8eGwTuelSXjoW3HYpsrh06NBBJk+eLEuXLpXmzZvL49J2+Oihe8eOHTJ//nzZvn27uAtCNwAAAAB4MG0B17WpbUP43bt3TSDWSc60/VtnIledOnUyM46fP3/ezFyu9HGhQoWs7eX6WNvLtVI8ZcoUWbBggVSrVi3Ry5vFxcfHR+bNm2dmTn/++eets5x/9tlncZ4T22talVe6pnj0ar9OADdnzhy7Kr2rEboBAAAAwIPpUl22dGIzi/r169u99vnnn0v69OljXdYruvv375t1sOMLsBrUkyJv3ryycePGRE3+9ii06u6saz8qQjcAAAAApBA6mVliaTD39/d3+D2kcmIodrfArQjd8Fqn0+cQd5Pv1kVX3wK87M+Uu+LPevL/mXLU73x0u8cfY2cRNGupw67lqPty17/Hjvrn566fD3A0R/1Z579XSA7u9zUAAAAAAABegtANAAAAAICTELoBAAAAAHASQjcAAAAAeAldOmv//v3xHqPrd+us5I9L1wMPCwtL8v2NGDHCzIweH72/pNyj3kt8rl+/LuPGjRNXIHR7EJ2qv3fv3maLiIhw9e0AAAAAcDO7d++WWrVqya5du+I85s0335RixYpJvnz5JG3atGZdbttN1/f+4IMPEnyvyMhIqVKlimzfvj3R9/fJJ59IeHi43ZJlupZ4q1atzGMN4xrMJ0+ebNbzTiw99sMPP4zzdV0m7ccff5RZs2ZJcmP2cg8SGhpqfazBGwAAAEDKdffuXbl27ZqkS5fOhGdVqlQpef/9981a2PpYaYi9d++eOT5btmwyb948s//XX3+VPn36yObNm+2u261bNwkMDIzxfhUqVJAbN25IlixZxMfHR+7cuWOCe+fOncXX19ccc/v2bblw4YJMnz5dGjdubHe+hm1dQ3zHjh3meYcOHWTbtm3mmhrgy5Yta9b9Xrt2rXz99ddy9epVKVOmjNm3evVqKVq0qDnv0qVL5rNkypTJeu09e/aY9cq1om1b/dZrPPHEEybkz5kzR2rWrClNmzY1v4fkQugGAAAAAA+0bt06adKkiQmUltCtNAxrKA4ODraGbq0ga/D+888/TZXbEoK1Iq7VbYuRI0fKoUOHZNGiRSa8T5s2TerXr29e0yCv19LQbakuly5dWvr372+tXOt7a8t55cqVY9zvkCFDTKVbvyRQX331lZw+fVpWrVoly5Ytk7Fjx5p1xLds2SIFChSQpUuXmqCsAdwSuNVnn31m7tMS9PWzaSt6r169zBcAGsZ1vW69V70f/ZwFCxaU7Nmzmy8UtM38448/luRC6AYAAAAAN6JBUTdbGjAtIdOiUaNG8uDBgxjnt2vXzgTrkJCQeN9Hg/V3331nKr86fLV48eJSr149efnll83rGqgLFy5sPX7UqFGyePFiWbFihQnmBw4ckIkTJ5q2bh0nPn78eGnfvr0J0r/88oukTp3aeq4Gfg3tU6dOte47ceKEvPDCC6Y6rZXu5s2bS+vWrU1r+bfffmv2f/PNN/LGG2/Y3fegQYPMZqFBW2k4/89//mPuOygoKNbP/Nprr0nDhg0J3XA8/ZZLWz1SijFjxrj6FgAAAIBHHlY6cOBAu30DBgxIMEQnxd69e+Xo0aNy5swZ04augVvDdq5cuczrGub1Na04W2jQ1Qq6tqV37NjRhFe9Tw3YOl5aw7h+WbBmzRq7wK1OnjwpRYoUsavIa+jWIK+V7U2bNpnKuraa61BazS9avf7rr79MINcw/dtvv5l2c1t6/oYNG2TSpEnmuf6OqlevLhUrVrRW6G0FBASY62p13HZcuTMRulMIDdwEUQAAAMD9aeCMXqmNXuW2VaJECTly5IgJxLYzeQ8ePNj6XAO1VrUtYVdbw7W9u3bt2mbyNa1Y63hnbT/X6x07dkzy5s1r2rRt9ezZ04zZ1nO10q3jr7UFXYO3jqk+e/ZsrGE2IiLCBF5bGqY1ZGvo1+38+fMmtPfo0UP++9//yvr16031fP78+WbCNg3s2jKun1Ovp+FcA7veu34J8NZbb8lHH30kCxcuNBX0tm3byttvv23Xmq5y5sxpPkOePHkkOTB7OQAAAAC4EQ3Y/v7+dlt8oVtXOfrhhx9M9VY3baHW9mnLc52J3DKO+u+//zaV4H79+pnqtoZSnaRMx3Y///zz0qBBAzO++tVXXzWzoFtoGNax0DpWW4O2btr6reFdZx//97//bcZg6zhzDcj6JYCtjBkz2k1yprR1/b333jPBWjetmh8/fly6d+8uTz31lLk3vV6NGjXk4MGDpp3+4sWLMmXKFBOkNdz/8ccfZsI2rZRrGNdQXrVqVfNFgt6zXmflypV276v3YTsJm7NR6QYAAAAADxa9Gh0bS7u3Vnd1pu/cuXObFnINyNqiraFYZyfXKrOOv9ZWbx3rbaFhVivIOqZbw7pWmPv27Ws2nZFcJyrTCvrcuXPNprOa28qfP79pZ7ellWudRVwnN1M//fSTuU8N1Sp6pVt/apVav1QoV66cCeMW2gZ/6tQpUz3Xid70OJ1YTSdc0+XCLPRLCJ3xndANAAAAAHAKDdw6k3eGDBms63ZrKNaluXSJLQ3mly9flq5du8qIESPMMVpFXr58uamY6+OZM2daW+G1HV1nUbd46aWXTJXZtjqfLVs2MzO5VsAt7d56Ha1W67rgSsOx7tPwrUNjtfVcK9/atn748GHzZYDS+9Zx71pl1xZ4nYBNg7QuI6Yt9Rq0ddy2Tgqn968h3ULHf2tLfXIidAMAAACAB9MqdHw0TEef2EzDs7ZkK60Ya7jVidCGDRtmKsxa6da2dQsNuVrB1iBtGTuuNOy2aNHC2r5uGUOuz3///Xe799QQr6FYx4BbaHXcMvZcW991sjWtsOtmW+nWdnhLxVoDtU7WpoFa280t4V5b6i1LhelEcdr+3rJlS9MSX7JkSeuyZbYznycHQjcAAAAAeDBLBdg29Grw1GW8dPksnTRs1qxZj/UeOit4bLOna5V6yZIl1rW/49OhQwezHJiO/dbqtNJArOFa6Rretl8gaKXbEvCHDx9u3f/ll19ag7SORdequk7Cpi3lSivlGrY10OtyYjq5mx47YcIEU8Wn0g0AAAAASDSt+OoSWbYhXFutNRDrJGfa/q1t2KpTp05mEjQNqZbwqY8LFSpkbS/Xx9peroFXx1cvWLBAqlWrlug1xePi4+Mj8+bNM23hOmmb7ZLGuv/zzz8348Mta43r+PHPPvssxnU04Oss5ZbJ2PTz6Thz27Hiffr0MePKLefr59LfhVbzkxuhGwAAAAA8mI55tmUJrir6WtUabLVNOzFrVOukY9rKbdtmHp0G9aTImzevbNy40bSB6+Rolip3q1atzFhwy6RwOlP61KlTY5213VIlVzpp2o4dOxJ8Xz1OlzVLzKRzjkboBgAAAIAUQiczSywN5rpcmaOliiX4Rv8SQCdeS473TQ6Ebg+l3zbpYvCJpYvH49Hku3XR1bcAN+GOfxZOp8/h1Z/P23n773x0u/9VIh5X0KylbndPAFzH2//9Ce9C6PZQOntgUiQloAMAAAAAHMM19XUAAAAAAFIAQjcAAAAAAE5C6AYAAAAAL6HrXO/fvz/eY3T9bp2V/HHpeuBhYWFJvr8RI0aYmdETsm3bNpk+fXqi7yU+169fNzOiuwKhGwAAAAC8xO7du6VWrVqya9euOI958803zVrXuo61rmmt63Lbbrq+9wcffJDge0VGRpplv7Zv357o+/vkk08kPDzcOlt59DW+bcO4HvfNN98k6rq69veHH34Y5+u6TNqPP/4os2bNkuTGRGoAAAAA4IHu3r0r165dk3Tp0pnwrEqVKiXvv/++WQtbH1uqy/fu3TPH61Jc8+bNM/t//fVX6dOnj2zevNnuut26dZPAwMAY71ehQgW5ceOGWfPax8fHBGYN7p07d7aup3379m25cOGCqVA3btzY7nwN0bqGuO262hkzZjSBOHXq1Ob+9Jq6aSjXz5QpU6YY93Hp0iVzrO1re/bsMeuVa0Xbtvp99epVeeKJJ8z15syZIzVr1pSmTZs6ZUmyuBC6AQAAAMADrVu3Tpo0aWINqBYaWjUUBwcHW0O3VpA1eP/555+mym0JwVoR1+q2xciRI+XQoUOyaNEiE96nTZsm9evXN69pkNdraei2VJdLly4t/fv3t6tca8t55cqVY9zvkCFDTKVbvySwiK3NfPLkyXL06FFzfwcPHpSPPvrI7NcvEdq1ayefffaZuU9L0NdraLt8r169zBcAGsZ1TW69V70fvU7BggUle/bs5gsFbTP/+OOPJbnQXg4AAAAAbkSDorZu227R27BVo0aN5MGDB+Y1rfBatjZt2pj2cMtzrU7rMVr5tQRupcH6u+++k2PHjslvv/1mKsj16tWTNWvWyN9//20q0IULF7YeP2rUKKlTp4789ddf8ssvv8iBAwekR48epq27a9euJtS3b9/e7NP7snXv3j0T2rXKnJDcuXObLwI0JGfOnNk8Pnv2rCxfvty8PmjQILl165YZm66bhv+OHTuax2+88YaEhISYx1rl1sq7Bm6L1157TX744QdJTlS6Uwg/P78UtVb3mDFjXH0LAAAAwCMJDQ2VgQMH2u0bMGCACZOOsnfvXlNNPnPmjKkgFy9eXF5++WXJlSuXeV1Ds75WoEAB6zkadrWCrm3pGnIbNmxo7lMDuI6XXrFihQn3Gtq1XdzWyZMnpUiRInYVea3Sa1u4VqT1/bRyrRVrbXnXKrvu1y8KunTpYtrJY5u0Tb8I2LBhg0yaNMk8199R9erVpWLFitYKva2AgADzHlodt1TnnY3QnYL+4gIAAABwf9oWHhQUZLfP0kodmxIlSsiRI0dMILadyXvw4MHW5xqotaqtTpw4YarDX331ldSuXdtMvjZx4kQz3lnbz/V6Wv3OmzevadO21bNnTzNmW8/VSvfatWtNC7oGbx1TrRXp2MJsRESECby2li1bZn5qlTx//vzWNnKL8+fPW78E0LHrOv7b9npaVNyyZYu5d/0S4K233jLXWLhwobzwwgvStm1befvtt6Vo0aJ2182ZM6f5DHny5JHkQHs5AAAAALgRDdj+/v52W3yhW7tatWVaq7e6aQu1jlm2PNdWc8s4am0b10pwv379THVbQ+nq1avN2O7nn39eGjRoYMLwq6++amZBt9AKto6F1rHaGrR105nFNbzPnj1b/v3vf8vSpUvNOHOd0Vy/BLCVMWNGu0nOLLSCvWTJEunUqZP89NNPdrOV63huy3hz29A9ZcoUE6Q13P/xxx9mwrZNmzaZLx20Ol61alXzRYLe81NPPSUrV660e0+9j9gmaHMWKt0AAAAA4MGiV6NjY2n31uqutnTruGlt6daArC3aGmh1dnIdj63jr7W923b8tYZZrSAvXrzYhHWtMPft29dsOiO5jpvWCvrcuXPNprOa28qfP79pZ49OA/u//vUvU1XXseI6JvvFF180oVgDtVbWLaHbck39UqFcuXJSo0YN63W0Df7UqVNm/LtO9KbVbJ1YTSdc07HpFvolhF4rOUM3lW4AAAAASEE0cOskZRqUday4VtK1oqzt37ppENdZwG1n+NYqsk5kVr58eRNiZ86cadq5dYy2tqN3797durb3Sy+9FGNW8mzZsplJ0Wwr4DpmXNvB9f2mTp1qqvU63lrnZ9q3b5+ZCM0yC7ptpTtDhgxm+Oy3334r69evN9e1LDlmaanX6+ikcPpFgS0d/60t9cmJSjcAAAAAeDCtQsdHZ/qOPrGZtptrS7bSivHx48fNRGjDhg0zQVYr3dq2bqEhVyvY2uZuGTtuCcMtWrSwWwZMx5Dr899//93uPXXstoZiHQOutJKt4Vjbv8uWLWuWH9NlxbR1XdcO19Z3y33bhm49RydrGzFihFy8eNHaeq9fEliWCtOJ4rRVvWXLlia4lyxZ0hyj19cJ4ZIToRsAAAAAPJi2g9vS0KvBU5fN0iCrk4bNmjXrsd5DZwWPbfb0rFmzmjHZtkuRxaVDhw5mDW4d+928eXOz6WRp0b8Q0DZ1HWf+3nvvmRnLtTquIVyr6erLL7+0Bmkdi65jt/U62lKudGy4hm29jlbsdXI3PXbChAmmvZ5KNwAAAAAg0bTiq0tk2YZwbbXWQKxjprX9W8dMK52wTCvJGlIt4VMfa3u5VsS1pVsfX7582VS0ddKyBQsWSLVq1WJ9bw28sa0hHhsfHx+ZN2+emXxNJ23TidCiB+6dO3ea9cN//vlnU8HeunWrCcu6bNgzzzxjjtGAb5npXNcR18+n48wttOVdlx3TMeCfffaZ2aefS38XWs1Pbj5RCfUiJJJO187ayPb4nbjWH4dPOOQ6+W5ddMh1AGc4nT6Hw67ljn/WHfn53BG/88QLmrXUIdcZ3a65ePOfBXf954fk547/fkHi5S5fyWnX1jZtHZOdmDWqdVy2tnJrm7lt+/jjevjwYbyTvzlrDe2E3tdZqHQncp2827dvJ/k8XTsOno//gUFKwZ/15OeOX5o48n/UHfn5HBWWHRXeHa1hYAkHXemCg64j0jjwn/GPgDty1L9fUuKXEzrpWGJp8NVJ1hwtVQLB1xmBOzHv6yyE7kTQwP0oFWutdAMAAAAAUi6WDAMAAAAAwEkI3QAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAPBAISEhZu3rpUv/WZlB17PW59OnTzfP16xZY2bsPnfunN15Fy9elBdeeEEyZsxo1t/es2eP3fV0nWtd93r48OHioBWmUzRCNwAAAAB4sP3795uf+/bts9u/atUqE5p//vlnu/0dOnSQBw8eyO7du6V169bSqlUrsza2atKkiRw/flz69+8vQ4cOlSFDhiTjJ/FOhG4AAAAA8FCpU6e2hu2wsDDz3ELDdr169exC9+HDh2X58uUyZcoUKVasmPTt21cuXbokv/32m3ldq9x58+aV9u3by8iRI+Xzzz+Xhw8fuuCTeQ9CNwAAAAC4kTt37khkZKTdpvtiU6VKFWvoPnjwoFSuXNk8Pn/+vKlk9+nTxy50b926VYoUKSIBAQHmuYb0nj17ip+fX4xrN2rUyFxHK9/x0ffRVvUTJ05YK+mvvvrqY/wGvEsaV98AEBwcLLdv33boNceMGePQ6wEAAADJJTQ0VAYOHGi3b8CAAWbMdXQ69nrZsmVy9uxZyZo1q6RPn97sX716tZQoUUIaNGhgxnDv3btXypcvb47LnTt3jGsry9hwiyeeeML8jIiIMEE9LhUqVJA33nhDPv74Y3nvvfdk8eLF1pZ3ELrhBjRwE5IBAACA/xWlgoKC7Pb5+vrGeqxWqgsXLmwCc2BgoKlMW8Zz16hRw1SwK1asaKrdGrrv3btn14IeH51UTSVmMrVPPvlESpcubaruOh78ySefTNR7pAS0lwMAAACAG9GA7e/vb7fFFbqVhu158+aZnxYasufMmWOq33/88YcJ4UqfX7lyJcb5c+fOjXFdy6znllb0+GTJkkVeeeUVMxO6Vr3xP4RuAAAAAPBgGprXrl1rDd03b96UM2fOyIYNG2TXrl1m0rRffvnFjAt/6qmnJDw8XK5du2aO1VnLjx07Jvny5YtxXQ3qefLkMZX0hJw8eVJmzpwpTz/9tKl6438I3QAAAADgwSxh2/Lz7t27Zmby6tWrS6FCheTFF180beWbNm2SWrVqmTbwt956S44ePSofffSRqX7rsUqP08A+e/ZsM7N5v379rG3m8XnnnXekS5cu8s0335iQr2Ef/yB0AwAAAIAH04nMtAVdA7b68MMP5dlnn7W+njlzZqlWrZqpXKdKlUqWLFliJkcrV66crF+/3kzEpkuFKX2s19FqtU7o1qtXrwTff/78+bJ9+3Yzllsr5v/973+la9euLDX2/5hIDQAAAAA8kO1s5levXjU/NUTHZvPmzdbHBQoUsI7xjn692GZIT8hLL71kNgsN/brhH1S6AQAAAADxjtfWFvTYtrZt27r69twelW4AAAAAQJx0+a+4xmhnyJAh2e/H0xC6PXz9Pl3j2tPpeBIASIny3bro6luAm2gcWNIh11mxJ9wh1wEAW2nSpLGOF0fSEbo9mAbuMWPGiKfr3bu3q28BAAAAAJyCMd0AAAAAADgJoRsAAAAAACchdAMAAAAA4CSEbgAAAADwQLqmto+PjyxdutQ8nzBhgnk+ffp083zNmjWSKlUqOXfunN15Fy9elBdeeEEyZswo1apVkz179thdL23atFK8eHEZPny4REVFueCTeRdCNwAAAAB4sP3795uf+/bts9u/atUqE5p//vlnu/0dOnSQBw8eyO7du6V169bSqlUruX//vnmtSZMmcvz4cenfv78MHTpUhgwZkoyfxDsRugEAAADAQ6VOndoatsPCwsxzCw3b9erVswvdhw8fluXLl8uUKVOkWLFi0rdvX7l06ZL89ttv5nWtcufNm1fat28vI0eOlM8//1wePnzogk/mPQjdAAAAAOBG7ty5I5GRkXab7otNlSpVrKH74MGDUrlyZfP4/PnzppLdp08fu9C9detWKVKkiAQEBJjnGtJ79uwpfn5+Ma7dqFEjcx2tfMfnk08+kaZNm9oFe73e1atXH/E34F1Ypxsup38hHb1WtzesXw4AAICUKTQ0VAYOHGi3b8CAAWbMdXQ69nrZsmVy9uxZyZo1q6RPn97sX716tZQoUUIaNGhgxnDv3btXypcvb47LnTt3jGsry9hwiyeeeML8jIiIMEE9Lq+88oppQ9cvB/z9/WXhwoUmsGfJkuUxfgveg9ANt/iXCgAAAIB/BAcHS1BQkN0+X1/fWI/VSnXhwoVNYA4MDDSVact47ho1apgCV8WKFU21W0P3vXv37FrQ46OTqqmEJlMrWbKklCpVSn788Ud57bXXZNGiRfLOO+8k8tN6P9rLAQAAAMCNaMDWirHtFlfoVhq2582bZ35aaMieM2eOqX7/8ccfJoQrfX7lypUY58+dOzfGdS2znlta0eOj1e4FCxaYc7StvUWLFkn6zN6M0A0AAAAAHkxD89q1a62h++bNm3LmzBnZsGGD7Nq1y0ya9ssvv5hx4U899ZSEh4fLtWvXzLE6a/mxY8ckX758Ma6rQT1Pnjymkp6QNm3ayIoVK0zQb9y4sWTKlMkJn9QzEboBAAAAwINZwrbl5927d83M5NWrV5dChQrJiy++aNrKN23aJLVq1ZLSpUvLW2+9JUePHpWPPvrIVL/1WKXHaWCfPXu2mdm8X79+1jbz+BQtWtS0meu4cw3g+B9CNwAAAAB4sAoVKpgWdA3Y6sMPP5Rnn33W+nrmzJmlWrVqpnKdKlUqWbJkiZkcrVy5crJ+/XozEZsuFab0sV5HZyTXuZd69eqV6PvQsK2Vc9uZzMFEagAAAADgkWxnM7csz6UhOjabN2+2Pi5QoIB1jHf068U2Q3pi6DJh2r7esmVLyZAhwyNdw1sRugEAAAAAcTp58qTdJG22dPy2TsKmY8V1KTKdwRz2CN0AAAAAgDg9+eSTZkK22Fiq2tevX0/mu/IchG4AAAAAQJzSpEljHS+OpCN0J+MC97dv33boNXXyA6Rc+W5ddMh1TqfPIe52T3DNPz9v545/Pr3975873lPDwBIOu1bjwJIOu1bQrKXibhz5+eC5/35xx7/HgKchdCcTDdxjxoxx6DV79+7t0OsBAAAAAByLJcMAAAAAAHASQjcAAAAAAE5C6AYAAAAAwEkI3QAAAADggUJCQsTHx0eWLv1nMsYJEyaY59OnTzfP16xZI6lSpZJz587ZnXfx4kV54YUXJGPGjFKtWjXZs2eP3fXSpk0rxYsXl+HDh0tUVJQLPpl3IXQDAAAAgAfbv3+/+blv3z67/atWrTKh+eeff7bb36FDB3nw4IHs3r1bWrduLa1atZL79++b15o0aSLHjx+X/v37y9ChQ2XIkCHJ+Em8E6EbAAAAADxU6tSprWE7LCzMPLfQsF2vXj270H348GFZvny5TJkyRYoVKyZ9+/aVS5cuyW+//WZe1yp33rx5pX379jJy5Ej5/PPP5eHDhy74ZN6D0A0AAAAAbuTOnTsSGRlpt+m+2FSpUsUaug8ePCiVK1c2j8+fP28q2X369LEL3Vu3bpUiRYpIQECAea4hvWfPnuLn5xfj2o0aNTLX0cp3fPR9tS3dduvYseNj/Q68Cet0I9kEBweb9cqTg6PXRAcAAACSS2hoqAwcONBu34ABA8yY6+h07PWyZcvk7NmzkjVrVkmfPr3Zv3r1ailRooQ0aNDAjOHeu3evlC9f3hyXO3fuGNdWlrHhFk888YT5GRERYYJ6XPR9Ll++bB7//fffUrt2bXn55Zcf+fN7G0I3ko0GbsIwAAAAkHCxKigoyG6fr69vrMdqpbpw4cImMAcGBprKtGU8d40aNUwFu2LFiqbaraH73r17di3o8dGKtUpoMjWdrE0Dv44T1wna3njjDWnatGkiP633o70cAAAAANyIBmx/f3+7La7QrTRsz5s3z/y00JA9Z84cE4b/+OMPE8KVPr9y5UqM8+fOnRvjupZZzy2t6AnRydc0eDP5mj1CNwAAAAB4MA3Na9eutYbumzdvypkzZ2TDhg2ya9cuM2naL7/8YsaFP/XUUxIeHi7Xrl0zx+qs5ceOHZN8+fLFuK4G9Tx58phKekK0xX3q1KkmvKdJQ0O1LUI3AAAAAHgwS9i2/Lx7966Zmbx69epSqFAhefHFF01b+aZNm6RWrVpSunRpeeutt+To0aPy0Ucfmeq3Hqv0OA3ss2fPNjOb9+vXz9pmHpdTp06Z2c7HjRtn1v7WSrpO/oZ/ELoBAAAAwINVqFDBtKBrwFYffvihPPvss9bXM2fOLNWqVTOVax1/vWTJEjM5Wrly5WT9+vWmSq1LhSl9rNf55JNPzIRuvXr1SvD916xZYyZra9OmjWTLls1sZcqUceIn9izU/QEAAADAA9nOZn716lXzU0N0bDZv3mx9XKBAAesY7+jXi22G9ITo8mAsERY3Kt0AAAAAgDidPHnStKDHtrVt29bVt+f2qHQDAAAAAOL05JNPmgnZYpMhQ4Zkvx9PQ+j2YLrmXu/evcVT6LgROM7p9DnEm3n750Py489U8nPP3/kFh11pxZ5wh10LANyZzkZuGS+OpCN0ezCd2MCTeNIXBAAAAADgCIzpBgAAAADASQjdAAAAAAA4CaEbAAAAAAAnIXQDAAAAAOAkhG4AAAAA8ALHjx8XHx8fp77H4cOHJXv27DH2b9iwQUqXLi05c+aU0aNHO/UePA2hGwAAAACQoKNHj0qTJk3k8uXLdvvPnz8vLVq0kFdffVW2bt0q3377raxbt85l9+luCN0AAAAAgAQ1b95c3nzzzRj7NWQ/+eST0r9/fylevLh8/PHHMnXqVJfcozsidAMAAACAG7lz545ERkbabbovNj/++KMUK1bMtHXPnDnTuj8sLExq164tWbJkMdXp06dPm/3r16+XQoUKyZIlS6RgwYKmVXz8+PHW8/bs2SPlypUz+3v37i2lSpWScePGWd/rpZdeinEPu3fvlvr161tb26tVqyY7duxw+O/FU6Vx9Q0g5fDz8zN/cZPDmDFjkuV9AAAAAEcLDQ2VgQMH2u0bMGCAhISE2O07d+6ctGnTRj777DMTel977TWz//r169KwYUPp2rWrqUIPHTpUWrZsKdu3bzevX7x4UYYPHy7Lli0zbeB9+vSRLl26mP9f79atm2kTb9asmTzzzDOyfPlyKVOmjDmvcOHCZtx4dPqlgOUY5e/vL2fPnnXK78YTEbqRrP/yAAAAABC/4OBgCQoKstvn6+sb47gVK1aYIPyf//zHPNdQ3rRpU1m6dKlkzpzZBHU1duxYyZUrl2zbts0ayidOnChly5Y17eDvvvuuCfBa+d61a5dMnz5dSpQoYV4/efKkPP300/Heb5o0aezuT8P7zZs3HfK78AaEbgAAAABwIxpgYwvZ0f31119SoEAB6/OiRYuan6dOnTJh3DYE582b1wTo3LlzS7Zs2SQwMNC8li5dOvMzKirK/NRWdZ0MTdvVDx06ZFfBjou2outkahbXrl2zXheM6QYAAAAAj6QB2raNW0O10iB+7Ngx634dD67HaSXb0v4dGw3eGrJ79OghefLkkfbt20uFChUSvI+qVauaoG6xc+dOE/LxD0I3AAAAAHggHbd98OBBmTFjhhw5csQ65lvHY2u1WceFnzhxwoRobSPXcByf8PBw2bhxo2zevNlcL7HrbetyYXrO6tWr5d69ezJixAhp1KiRQz6jNyB0AwAAAIAHypcvn5koTcO1zlRuGXudKVMmWblypaxatUrKly9vKuCLFy+WVKnij386jlur53Xr1jXt6enTpzcTqyVEW9F1ImOdJT0gIMCE948++shhn9PTMaYbAAAAADxU69atzWahVWalYVurz9HVq1cvxgzklvHcura2hm5dTixDhgxm+TAN0npNS0u6LjdmOd6WhnOtbmvlXWc91+CPfxC6AQAAAABm2bHZs2ebdbpv3bplxoAPGzYszjHg0Wl13HYCN/yD0A0AAAAAkCJFisiaNWtcfRteh9ANwC3lu3XR1bfgEU6nz+HqW4CX/Vnw9r97jQNLijtyx/sKmrVUvFnDwBLibtzxzwGAx8dEagAAAAAAOAmhGwAAAAAAJyF0AwAAAADgJIRuAAAAAACchNANAAAAAHCJFi1aiI+Pj3Vr0KCBeBtCNwAAAACkMIUKFZL169e7+jbk999/l71798rly5fNtnjxYvE2LBkGAAAAAEh2Z86ckaioKClXrpx4MyrdAAAAAOBG7ty5I5GRkXab7otNx44dJSQkRGbNmiUlS5aUiRMnmv3bt2+X6tWrS5YsWaRVq1Zy9epVs79x48amjfvEiRNSv35983jYsGHmtenTp0u9evWs1z5+/Lh53UJf02NGjx4tBQsWlGXLltmdN3nyZAkICDDbggULEvyc27ZtkwcPHki+fPkkY8aM0rZtW1Pt9jZUuuESwcHBcvv2baddf8yYMU67NgAAAOBMoaGhMnDgQLt9AwYMMOE6NitXrpQVK1bIqFGjpFKlSnLlyhV5/vnnpXv37jJv3jx54403pE+fPjJlyhT54Ycf5N69exIYGCgTJkyQ2rVrS/r06RN9b19++aVkzZrV/Kxatap1f1hYmAnamzdvNu/Tq1cvE/bjc/DgQalQoYK571SpUkmXLl1MTpg0aZJ4E0I3XEIDN8EYAAAAiEmDZ1BQkN0+X1/fOI8/cuSIHDp0yFS11bfffitp06Y1QV0r1X379pX27dub17SirDTkZsqUyQTopLh+/bps2LBB0qVLZ7f/xo0bMmPGDMmdO7d07txZhg8fnqjPGRwcbH0+cuRIE9QJ3QAAAAAAp9GAHV/Ijq5Dhw7WwK1Onz4t58+fl2zZspnnDx8+lGvXrpnCl5+fX6Kve/PmzRj7unXrFiNwq9KlS5vArWJ7PTH0/IsXL5pW+qR8fnfHmG4AAAAA8GCW6rWFjpGuXLmy7Nq1y2y7d++WnTt3muq3hVa6dRIzW1oV14BusWPHjgTfy8Lf3z/J992mTRvZtGmT9fnWrVvNeHBvCtyK0A0AAADg/9p7E3gp5/f//x3RIi1KUamopGhRUhIqpIVslQiRrCVUaJHKVlJCES2EtApt2glZkpSEdpUkibTKJ8z/8by+//v85kxzzpk5554zM+e8no/HfWbmnpn3vO/73Mv7el/X9bpEDqJly5Zu69atJlRGvvbbb79tAmrBRnbFihXd/Pnz3S+//OIWLVpk68qUKeO+//57E27DUz548OCY9rN69erugQceMMP7vffes1Dzu+++2+U0ZHQLIYQQQgghRA6CPO0ZM2a4oUOHutNOO81NnTrVXufNmzdV/vTs2bNduXLlUgTamjRp4po2bWrGMIZ77969Y9rPhx9+2ATdmBDA2L7nnntcnz59XE5DOd1CCCGEEEIIkaRQriscKIsvXbo0ze/VqlXLrVq1KtU6Qs4nTJiQat3111+f8nzx4sVpli1j8ahQocIRoevhINx97NixtuRk5OkWQgghhBBCCOE7hLgXLVo07EJN7tyCPN1CCCGEEEIIIXyndOnSJuQWjoIFC7rcgoxuIYQQQgghhBC+Qw55hQoVXG5HRrcQIiHZVqC4L+2U/et3l2h9EsmNjikhcgbzV63zpZ2mNU73pR0hRM5FOd1CCCGEEEIIIUSMkKc7huTPn9/qzsHOnTvj3R0hhBBCCCGEENmMjO4YMnDgwJTnnvEthBBCCCGEECL3IKNbRE2vXr3coUOHstSGPP9CCCGEEEKI3ICMbhE1GNzDhg3LUhvy/AshhBBCCBE/Fi9e7G655Ra3efPmqL973XXXuZIlS7rhw4fHpG85DRndQgghhBBCCJHLaNiwoVu1alXU33v//ffNYF+7dm1M+pUTkXq5EEIIIYQQQuTCGtqFCxeO6jsHDhxw99xzj2lXFS1aNGZ9y2nI6BZCCCGEEEKIBOLvv/92e/fuTbWwLhyEiPfv39+NHz/eValSxY0cOdLWL1y40FWrVs0VLFjQNWjQwG3YsCHV9/BWV6hQIap+DRgwwP3vf/8zg33BggXuv//+y8JW5h4UXi5yjDhbMFnNORdCCCGEECJe4EnGwA2mX79+ZlyHY968eW7u3LluyJAhrnbt2rbuxhtvdN27d3ft2rWztvr27esmTpyY6T5t2bLFPf/88+6cc85xmzZtcs8995wrW7ase++999xRR8mXmx4yukWOEWcTQgghhBAipzikunXrlmpdvnz50vz8xo0b3fr1612RIkVS1hUoUMAdPnzYnXDCCW7UqFHun3/+yVKfXn/9dVeqVCm3aNEilz9/fjPoy5cvbx71pk2bZqntnI6mJIQQQgghhBAigcDAJt86eEnP6O7QoUMqgxsIN//www9dmTJlXOPGjd3q1auz1Kdt27a5Sy65xAxuOP74413lypWPCFsXRyKjWwghhBBCCCGSmOOOOy7V64MHD5pnm7zrXbt2mVI5ud9ZgVDyv/76K+U1+dwY4hj1In1kdAshhBBCCCFEDgKD+7LLLnNvvfWW27lzpwsEAlkOL2/Tpo2bMWOGmzZtmhnbhMATvo73W6SPcrqFEEIIIYQQIgdBODrh5YinderUyVWqVMm9/PLLWWqzatWqJsRGm+vWrbM2p0+ffoSXXRyJjG4hhBBCCCGESFLGjRsXdn3r1q1tSYtGjRq5zZs3R/VbrVq1skVEh8LLhRBCCCGEECKXs3XrVle0aNGwC2XHROaRp1sIIYQQQgghcjmlS5d2K1euDPtewYIFs70/OQkZ3SIuUGrggQceSHmNwIMQwZT963eXaCRin7YVKB7vLuQ6tM+FELFi7qq1vrXVrEYV39oSuYO8efO6ChUqxLsbORIZ3SIuDBw4MNXrYANcCCGEEEIIIXIKyukWQgghhBBCCCFihIxuIYQQQgghhBAiRsjoFkIIIYQQQgghYoSMbiGEEEIIIYQQIkbI6BZCCCGEEEIIETc2bNjgTjjhhCPWf/TRR65q1aquRIkS7tlnn3XJioxuIYQQQgghhMhlUB5s8eLF8e6G27Rpk2vRooXbvXt3qvW//faba9Wqlbv++uvd559/7t566y334YcfumRERrcQQgghhBBCiLhwxRVXuDvuuOOI9RjZpUuXdn379nWVK1d2jz76qBs7dqxLRmR0CyGEEEIIIUQC8ffff7u9e/emWlgXjltuucX179/fjR8/3lWpUsWNHDnS1i9btszVq1fPFSlSxF1zzTVuz549tr5Zs2YuT548bsuWLa5x48b2fNCgQfbeuHHjXKNGjVLa3rx5s73vwXt8hlDv8uXLu/fffz/V90aPHu1KlSplyzvvvBPRts6aNcu1bt36iPXffPNNSv/g3HPPdcuXL3fJSN54d0AIyJ8/v3vggQd8a2/YsGG+tSWEEEIIIUR2MnDgQDdgwIBU6/r162fGdTjmzZvn5s6d64YMGeJq167t/vzzT9e8eXPXpUsXN2XKFHfbbbe57t27uzFjxrhp06a5w4cPuxo1ariXXnrJNWzY0BUoUCDivr3yyiuuaNGi9li3bt2U9atXrzZD+9NPP7Xfuf/++83Yz4hTTz3VjPtQmGioVq1ayuvChQu77du3u1xtdPttNCUSO3fujHcXcsWFRQghhBBCCOFcr169XLdu3VKty5cvX5qf37hxo1u/fr15tb3Q7GOOOcYMdTzFPXr0cDfffLO9d9xxx9njUUcd5QoVKmQGdDTs37/fBM6OPfbYVOsPHDjgXn/9dVeyZEnXsWNH9/TTT7uskDdv3lTbjL158OBBl6uN7pxsNCXCZAIn3qFDh1wioEkIIYQQQgghYgfGZnpGdigdOnRIMbhh27ZtJkRWrFgxe/3ff/+5ffv2mT2B8Rop4Yzcu+666wiDG1AZx+CGcO9HC2rmbIMH/fej3Xig8PIkgRMkUUKmE2ESQgghhBBCCOFSea89ypYt6+rUqeMmT55srwOBgOV04/32wNPN+mDwimOge4TLoQ79reDwbz+pW7eumzBhQsrrFStWuDJlyrhkREJqQgghhBBCCJGDaNmypdu6dav78ssvLV/77bffNgG1YCO7YsWKbv78+e6XX35xixYtsnUYtd9//73lU+NlHjx4cNy2oVWrVpYfvnDhQstBpy+XXXaZS0ZkdAshhBBCCCFEDoI87RkzZrihQ4e60047zU2dOtVekyft8cwzz7jZs2e7cuXKpQi0NWnSxDVt2tRVr17dDPfevXvHbRtKlChhkb7U8EYNfe3ate6RRx5xyYjCy4UQQgghhBAiSaFcV1rh2UuXLk3ze7Vq1XKrVq1KtY6Q8+CQbrj++utTni9evDjNsmUsHhUqVDgidD090vo8+eN4t9esWeMuuOACE35LRmR0CyGEEEIIIYTwHULca9SoEfY9wt0nTZoUUUkxlmRGRrcQQgghhBBCCN8pXbq0W7lyZdj3ChYs6HILMrqFEL6xrUDxeHdBiJhT9q/fE/Kc8bNfQkDTGqf71tb8VetcTsXPbfNznwuRCJBDXqFCBZfbkZCaEEIIIYQQQggRI2R0CyGEEEIIIYQQMUJGtxBCCCGEEEIIESNkdAshhBBCCCGEEDFCRrcQQgghhBBC5DKouR2tyNmoUaPcySef7I455hh30UUXuV9++eWIzxw+fNhVr149zZreuREZ3UIIIYQQQgiRy2jYsKFbtWpVxJ9fsmSJ69u3r3vzzTfdjz/+6AKBgOvRo8cRnxs8eLBbvXq1z71NbmR0CyGEEEIIIUQuLOdVuHDhiD+/fv1698orr7hLLrnElS1b1t16661uxYoVR3xmyJAhKhMWgoxuIYQQQgghhEgg/v77b7d3795UC+vCccstt7j+/fu78ePHuypVqriRI0fa+oULF7pq1aq5ggULugYNGrgNGzZkKbwcI/uqq65Keb127VpXuXLlVJ+58847Xc+ePV358uWj3OKcTd54d0DkTnr16uUOHToUs/aHDRsWs7aFEEIIIYSIJQMHDnQDBgxIta5fv35mXIdj3rx5bu7cueZlrl27tq278cYbXffu3V27du2sLULDJ06c6Ev//vjjD/N6T5gwIWXda6+95vbs2WMh53PmzPHld3IKMrpFXMDglmEshBBCCCFEeAdVt27dUq3Lly9fmp/fuHGjhXYXKVIkZV2BAgVM1OyEE04wAbR//vnHt/517tzZvOfNmze317/99pv1GeP/6KOP9u13cgoKLxdCCCGEEEKIBAIDm3zr4CU9o7tDhw6pDG4g3PzDDz90ZcqUcY0bN/ZN3Oz111+3dl999dWUdffff7+77bbbXM2aNX35jZyGjG4hhBBCCCGESGKOO+64VK8PHjxonu0FCxa4Xbt2mVI5ud9Z5auvvnL33nuvmzRpkitVqlTKesLMhw8f7ooWLWoLSueXX365GzRoUJZ/Myeg8HIhhBBCCCGEyEFgcF922WVu7Nix5uWmvFdWw8t37tzprrjiCvfQQw+5c845x+3fv9/WFypUyEqIBUMeOd7vZs2aZek3cwrydAshhBBCCCFEDoJwdMLLn3jiCVexYkU3c+ZM9/LLL2epTUTYduzYYYJsxx9/fMoCqKAHL/nz53cnnXSSeb2FPN1CCCGEEEIIkbSMGzcu7PrWrVvbkhaNGjVymzdvjvh37rvvPlsigXJk4v8hT7cQQgghhBBC5HK2bt2akpMduhAuLjKPPN1CCCGEEEIIkcspXbq0W7lyZdj3ChYsmO39yUnI6BZRQ47GAw88kGUhBiGESEa2FSge7y4IkXQ0rXG6SzTmr1oX7y4IkVDkzZvX8rGF/8joFlEzcODALLeRVaNdCCGEEEIIIZIB5XQLIYQQQgghhBAxQka3EEIIIYQQQggRI2R0CyGEEEIIIYQQMUJGtxBCCCGEEEKIuPHrr7+6L7/80h04cMDlRCSkliSK31L7FkIIIYQQQuQ0nnvuOde/f38rWfbLL7+4GTNmuAsuuMDe++ijj9xdd93lfvvtN9e7d2/XrVs3l4zI6E4SxW+pfQshhBBCCCH8gvJg48aNc40aNYpbHzZs2OAGDRrkvvvuO1emTBkzvvv27esWL15shnarVq1c9+7d3fXXX+/atWvnzj77bNe4cWOXbCi8XAghhBBCCCFEtvP333+7UaNGmcENtWvXdr///rs9f+utt8z7jRFeuXJl9+ijj7qxY8e6ZERGtxBCCCGEEEIkmDG6d+/eVAvrwnHLLbeYh3j8+PGuSpUqbuTIkbZ+2bJlrl69eq5IkSLummuucXv27LH1zZo1c3ny5HFbtmwxrzHP8TZDqOd78+bN9r4H7/GZZ5991pUvX969//77qb43evRoV6pUKVveeeedDLfzzDPPNG82kM/94osvuquvvtpef/PNNyn9g3PPPdctX77cJSMKLxdJmeOeEcOGDYtZ20IIIYQQQsQ6NXXAgAGp1vXr18+M63DMmzfPzZ071w0ZMsS8xX/++adr3ry569Kli5syZYq77bbbLEx7zJgxbtq0ae7w4cOuRo0a7qWXXnINGzZ0BQoUiLhvr7zyiitatKg91q1bN2X96tWrzdD+9NNP7Xfuv/9+M/YjAeOd8HEMeTzbwERDtWrVUj5TuHBht337dpeMyOgWSZnjLoQQQgghRE6lV69eR4iG5cuXL83Pb9y40a1fv9682l5o9jHHHGOGOp7iHj16uJtvvtneO+644+zxqKOOcoUKFTIDOhr2799vAmfHHntsqvV4ql9//XVXsmRJ17FjR/f0009H3GbTpk3dzJkzbZKAbWfyIG/evKm2GafdwYMHXTIio1sIIYQQQgghEgiMzfSM7FA6dOiQYnDDtm3bTIisWLFi9vq///5z+/btc4cOHTLjNVLCGbmoiYca3FC1alUzuCHc++mBgX3RRRe5F154wbzjGN0nnHCCbYMH/Y+23URBRrcQQgghhBBCJDGe99qjbNmyrk6dOm7y5Mn2OhAIWE433m8PPN2sDwavOAa6R7gc6tDfCg7/jpbJkyfbBAGh74BRffTRR9tzQtcnTJiQ8tkVK1akCK4lGxJSE0IIIYQQQogcRMuWLd3WrVvdl19+afnab7/9tgmoBRvZFStWdPPnz7fa2IsWLbJ1GLXff/+95VPjZR48eHBM+1mlShXLU3/33XdNtI089jZt2th7CKyRH75w4ULLQacvl112mUtGZHQLIYQQQgghRA6CPO0ZM2a4oUOHutNOO81NnTrVXhPG7fHMM8+42bNnu3LlyqUItDVp0sTyq6tXr26Ge+/evWPaz1q1apkgG/nr1OBGSI0+Q4kSJUwcuUWLFqaGvnbtWvfII4+4ZCRPIDSmQBwBKtvxVsNOhD4kG19v2BLvLgghRLZR9q//q2uaSGwrUDzeXRBZYO6qtS4nM3/VOpdoNK1xum9tNatRJcdeW/y8viTq9pWsXjveXUgYfvzxR7dmzRp3wQUXmPBbMqKcbiGEEEIIIYQQvkOIe40aNcK+R7j7pEmTMmzj1FNPtSWZkdEthBBCCCGEEMJ3Spcu7VauXBn2vYIFC7rcgoxuIZI07EgIv8N3E/FYV3hy9qN9LvwOT07UkHc/Q7kTMeQ9Ef9/IvdBDnmFChVcbkdCakIIIYQQQgghRIyQ0S2EEEIIIYQQQsQIGd1CCCGEEEIIIUSMkNEthBBCCCGEECJq/vvvP7d69eqovhMIBNzgwYPdP//8k+7nnn32Wff333+7nICMbiGEEEIIIYRIYl555RV32223pVp36aWXuq+++ipd4/fw4cNu7969btu2bW7VqlVuwYIFbty4ca53797ukksusXbTg++ec845btmyZRH39fHHH3dr1641kbVQDh486H7//f+EXQ8cOOA6d+7scgJSLxdCCCGEEEKIJCZfvnyuWLFiKa9nzpzpPvroI3fXXXelrHvuuedcw4YN7flvv/3mypcv7/799193/PHHu8KFC7v9+/e7/PnzuyZNmriTTjrJXX755a5cuXKpfqdmzZpmDBcpUsTlyZPHPNFly5Z1HTt2tD7AoUOH3K5du8x4pxZ3MBjbU6dOdcuXL7fXTzzxhPVr4sSJNknAI8Z+8eLFXd++fd3FF1/sPv74Y3fhhRe6ZEZGtxBCCCGEEEIkIb/88ovbsGGD27hxo9u+fbsZ2meddZZ5qrdu3WrG8xtvvOHGjBmTYnDDiSeeaEb2UUf9v8DnRx55xDzeGMtp8cknn5iHHKMb2rZt66pWrWoGsue5xhAn5LxOnTpHfP/JJ580T/exxx5rr+nX6NGj3ahRo8zops9MBngMGTLEde/e3X3wwQcumZHRLYQQQgghhBAJBIZraD4znmTPm+zx9ddfW340nmuMaIxvPNA9evQwg5uc60GDBrk333zziN/AI43xGy7MG/CC04eCBQumMoKnT5/u5s6d69avX+9++OEHN3LkSNenTx/3559/uhEjRribb77ZjHc81EcffXTKdwllx2gfO3bsEd5zvvfZZ5+58847L9V7Z599tnnNd+7c6UqWLOmSFRndOZhevXrZyZQbGTZsWLy7IIQQQgghRKYYOHCgGzBgQKp1/fr1c/3790+1rmXLlrbgnca7zHfuvfde17VrV/fMM8+YwYpnukOHDmaQYyRXqFDBvouHGkP5mGOOScmnxkifNWuWvUboDI/2li1bUn7vscceM6P+iy++cLfccotr2rSp/SYGNt/DGMdQX7RoUSqDG/Bin3baaSm/B/Tt119/tfD2efPmHbF9ULduXds2wt6TFRndORgMbhmfQgghhBBCJJ/zrFu3bqnWhXq5Q9mxY4ebMGGCeZoROHv77bfNA41B+/DDD5vgWTDBxnSk4eVw3333mTFPSDhGPKHfr732mhne5IET5h7Oe75z505XqlSpVOuYMLjooovc0KFD7TUGfSh4uPluMiP1ciGEEEIIIYRIIDCwMZaDl3BG9549e9z48eNNZRxv8KmnnprqfZTACxUqdETY+F9//WVe5ozgM3wWDzge7OHDh1uuNoY2C2Hr5HVj7N90000m4Pbhhx+agY9nPZjjjjvOQuCDeemllyw0nvYJM69WrZrlcAfDd0K3IdmQp1sIIYQQQgghkpCff/7ZQrpRCd+3b5/75ptvLMcaQ7lWrVruxx9/dO+++66JlSG6hho4hnPr1q1dgQIFUnmWQ8PLg43uFStWmMAZbZHTXblyZcvBJnecBUVy3sd7PmnSJFtQNQ/mlFNOcZs2bTpiG1BP3717txnslD3DECdk3TO0Md5DVdSTDRndQgghhBBCCJGE4BnG0+3ldOMl9jzFCJ3Vr1/fcqhnz57typQpk8rADiWj8HI83XPmzDHxNYx1VNG9UPjTTz/dtWjRIuWzGPV8Ptg7X6xYMTOwMaIrVqyYsp42mTRgYqB58+bWT4xwjG683KizV69e3SUzMrqFEEIIIYQQIgeBONmVV15p4mvU3m7Xrp0Zt1kJ00bcDQ82hnSwhxwPe6tWrVLKgAEec15/9dVXqdq48847rTY3OeAeiLJRRgxvORMF5KYXLVrU3iPXmxz1cLneyYSMbiGEEEIIIYRIUsjbJqwbtXCElDGM8T537tzZVMxh+fLlpgL+3HPPmeJ4OCMW73d6xi3K4uHUxTGQZ8yY4SpVqpRhXzt06GAebULJr7jiChNk877Xvn17E2LDY45HnD4jBrds2TKX7MjoFkIIIYQQQogkhXDyzz//3LzHeLaXLFnipk2b5ho0aJDymZdfftk81Q8++KCrV69eiifZywsnrPv77793Tz31lC81xdMiT548bsqUKSa+xm+WKFHCDHGoXbu2KZ97sC0TJ040T32yI6NbCCGEEEIIIZIUjG3PQ41BHVofG3i/d+/eVjos9H1yqDHGqd/dsGHDqH8fobVoKFOmjPvkk0/cUUelX0gL73lGn0kWZHQLIYQQQgghRJISHBIezuAOJq33KfeVnRwVgTGdUwxukNGdJBBW8cADD0T1nWQvIi+EEGX/+t3lZLYVKO4SjZy+zxOVRDwWEpFmNaq4nIyf29dt/Exf2ply7f8LURZCZA4Z3UkCORjREq2RLoQQQgghhBDCX3KOz14IIYQQQgghhEgwZHQLIYQQQgghhBAxQuHlQgghhBBCCJHEjBs3zvXs2dOdccYZYd9fuXKle+edd1yTJk3s9WOPPeZ++OEHK8nl8dNPP7ly5cq5vXv3Wp1s4R8yuoUQQgghhBAiicmXL58LBALun3/+Cfs+7xUoUCCVSPOxxx57RBsQvH7Pnj2uSJEiMet3bkFGtxBCCCGEEEIkMf/++68755xzXN++fcO+f88996QyyCnHFVxqLNjY9sqKffHFF65169bumWeecddff31M+5/TkdEthBBCCCGEEEnMmWee6erWrevmzp0b9v1WrVq5MmXKpGmwHzp0yLzhsGvXLjdo0CA3duxYd++997orrrgipn3PDcjoFkIIIYQQQogE4u+//7YlNPzbCwH3+PDDD80wLlq0qCtcuLB5sMOBQb1w4UL3xx9/uG+//dbWvf7667Z4rFixwh6rVavmrr32Wsv5Llu2bAy2LvchozsHQ65Gbq3VPWzYsHh3QQghhBBCiEwxcOBAN2DAgFTr+vXr5/r3759qXePGjd3q1avd0qVL3eeff55um02bNjWD2uPGG290o0ePtrBzPN1euPnXX3/tKlSo4Ov25HZkdOdgOFmFEEIIIYQQyUWvXr1ct27dUq0L9XIH88EHH7glS5a422+/PUXNvGTJkq5Fixb2+sknn3Qnn3xyKqOb3G2cdFCoUCH3559/2nN5t/1HRrcQQgghhBBCJBDhQsnTg7Dy5cuXu99//91eb9261b6/atUqe71hw4Y0Q89F7JHRLYQQQgghhBBJzCmnnGLh49TZhkWLFlmpLxTNgXDxE044IU0htTFjxqTU8Bb+I6NbCCGEEEIIIZIYBNEID1+3bl0qT7cXMo5hPX/+fHfxxRenEld77733rMzY/v37Tf08FN5fv369e/DBB7Nxa3IeMrqFEEIIIYQQIol57bXXXIkSJVJqbffo0cO82126dLHXKKFjeHssW7bMTZs2zdTP+/Tp4zp27GhiarBp0yZ3+umnu//++8+Meb4noztryOgWQgghhBBCiCSFEPLjjjvOHXPMMSnrtm3bZp5uPNUeGN6UFps5c6a78sorXZUqVVzv3r1TxNT4fsuWLV316tVTDHTE2GbMmBGHrcpZyOgWQgghhBBCiCTlq6++ivo7N9xwQ9j1s2bN8qFHIhRJ2AkhhBBCCCGEEDFCnm6RYyn71/+VTMgq2woU96UdIRL5OPcbv86bRN0+v/Bz+7TPkxvtd+E3U65t4Es7bad95nLy9iXqOK9kvDsgfEWebiGEEEIIIYQQIkbI6BZCCCGEEEIIIWKEjG4hhBBCCCGEECJGyOgWQgghhBBCiFwMpcU2bNgQ9feo5b169eqovhMIBNzgwYPdP//8k+7nnn32WStzlhOQ0S2EEEIIIYQQScpnn33m8ubN6ypUqOBOOeUUV6JECVs/aNAgd/zxx9v6woULu549e6bZRufOnd3vv0cv6Lh3716rE75s2bKIv/P444+7tWvXWp9DOXjwYEo/Dhw4YP3KCcjoFkIIIYQQQogkJV++fO6kk05ymzdvNuM3f/78tv7QoUPu5ptvtvU8eus97r//fnf66aeb0UwbGLg895ayZcu6999/P9V3atas6SpVquTq1Kljn7ngggvscx07dkz53llnnWX9mTt37hF9Xbt2rZs6daobOXKkvX7iiSdskmDBggX2euLEiW7//v32vG/fvu7HH390H3/8sUt2VDJMCCGEEEIIIZKUo446ykK2MVbxFHseZMK+MYo99uzZk+p7hG7fcccdrkePHmHbbdSo0RHrPvnkE/utIkWK2Ou2bdu6qlWrmoHs/S7t8tsY5qE8+eST5uk+9thj7fWYMWPc6NGj3ahRo9yll17qtm7d6sqXL5/y+SFDhrju3bu7Dz74wCUz8nQLIYQQQgghRJJCXvXOnTtd/fr1XYsWLWzda6+9Zh7iG2+80V6fffbZ7qWXXnKFChVy69ats3VHH320e/7551N5t4OX5cuXm0EfDEbwhRde6H755Rdr/4cffnBdu3Z1ffr0cXfeeac7fPiwedVZ9++//6b67uHDh81ob9my5RHe8z/++MPC5M8777xU79HvXbt22fYlM/J0CyGEEEIIIUQCgbc4VESMEHCWcEZ3qVKlzLu8Y8cOM76bNGni3njjDfNKY7ReeeWVtpDj7bXBexdddJG7/PLLw/bhscceC7suT5487osvvnC33HKLa9q0qRswYIAZ4LNmzbKQcvq9aNEiM+qD2bp1qzvttNPcMccck7KOPvz666+Wcz5v3jzXv3//I36zbt26tm1sU7Iio1skHL169bIclKwwbNgw3/ojhBBCCCFEdjJw4EAzZoPp169fWKMUozuUn3/+2V111VXu1FNPdccdd5z73//+57799lvL+caLndb3Imn7vvvuM0OekHA83YR+e551DPjt27eHFUnbuXOnTQ4Eg9cbw3/o0KH2GoM+lJIlS8rTLYTfYHDLaBZCCCGEELnZCdWtW7dU68J5uYE8bsK9ESTDc4w3u0GDBiZuVqtWLcvbfvvtt81o9wxuwBD/6KOP3Jo1a8K2+9NPP9lnPPBgY2hTyuvhhx92K1ascAsXLnQPPPCAmzBhgq3Hu/7hhx+aUNq0adNcxYoVU76P8e+JpHkQ8s4Ew7hx4yyUvFq1aq558+YpRjjwHcLikxkZ3UIIIYQQQgiRQKQVSh4OSmxhZJMv7YWXA8Z2+/bt3U033eSGDx9ugmTBjB079oic67/++stCvcN5ujHo3333XTd9+nRXuXJly8FGhI0FRXIE0LZs2eImTZpkC6rmwZxyyilu06ZNR/SfSYLdu3e7mTNnuttuu80Mcbz8nqG9ceNGV65cOZfMSEhNCCGEEEIIIZIUhNEwgkPBy40wGQb5vn37XIcOHVLew8ON0YyH3DNoe/fubR5sD5TFEUXzIPR7zpw5rnr16q5AgQLm1b777rstR5vSY126dLE2oHXr1u6ff/5J1Z9ixYqZgY0RHQxtNmvWzMTU8HaXKVPGjHDPy71hwwb7zWRGRrcQQgghhBBCJCmIltWrV8+ehyqG421euXKlPWLUegwaNMhqZqNO/ttvv9m6e+65xwxi2sDrfcUVV5j3nLBxIAwcpXGM+eAFg75Vq1ap1tWuXdtytUO58847rTZ3MIiy4Z0vWrSoW79+vXnreQ6EmWP4h8v1TiZkdAshhBBCCCFEEvLNN9+YYYyA2fvvv2954Bisq1atcm3atHGTJ0+25wULFjRRNUqIffrpp27JkiXu6quvTgkhZ+H9+fPnm+r4ZZddZh708ePHW6g4XmtE3Mj/5jcx5L0F7/WMGTNSreM3v/rqqyP626FDB2uXUHJAkK1SpUr2nFB4wsopSUablCwjFz2tOuLJhHK6hRBCCCGEECIJKV68uHvhhRcsJBuDmHxsRMx69uxp3u2lS5faOgxnynlhhBNOPnr0aDPOEUqrUKGClfLycqjxdLPg1ea7eKJD63VnVN4sLfLkyeOmTJni2rZta4JpiL95Ye94x1E+90D4beLEiS5//vwu2ZHRLYQQQgghhBBJCIb1XXfdZc8vvvhiW4Ca2aGGMnnTLNCuXTt7PPbYYy3MPD3SM7gB8bVoKFOmjHnnM2oX73lGn0kWcsZWCCGEEEIIIYQwEt1YPSqC/iX6NkSDPN1ZqJ1HPWnhPzt37ox3F4QQIuko+9fv8e6CEEIIIcIgozuTYHAPGzYs3t3IkTzwwAPx7oIQQgghhBBC+ELO8dkLIYQQQgghhBAJhoxuIYQQQgghhBAiRsjoFkIIIYQQQohczHvvvec2bNgQ9feo77169eqovhMIBNzgwYOt9ncsePbZZyMuYRZpX6JpMxwyuoUQQgghhBAiSfnss89c3rx5rd72KaecYrWvYdCgQe7444+39dTbpnZ3WnTu3Nn9/nv0gpx79+5155xzjlu2bFnE33n88cetTBl9jgUHDhyw7clqXw4ePJiyT6JpMxwyuoUQQgghhBAiScmXL5876aST3ObNm834zZ8/f4rw880332zrefTWe9x///3u9NNPN6OZNjAqee4t1AB///33U32nZs2arlKlSq5OnTr2mQsuuMA+17Fjx5TvnXXWWdafuXPnHtHXtWvXuqlTp7qRI0fa60aNGrk8efKkWpgkyOg9Hr3JBTj33HPtfejbt6/78ccf3ccff5zufgvtyxNPPGFtLliwwF5PnDjR7d+/P6o200Lq5UIIIYQQQgiRpFDPmjBpDES8s57XlrBvjGKPPXv2pPoe4dJ33HGH69GjR9h2MXpD+eSTT+y3ihQpYq/btm3rqlatakap97u0y29jmIfy5JNPmnf52GOPtdezZs2y0G7KMdO/l156KaU+d3rvAV7oX3/91ZUsWdJ9//33qX5nyJAhrnv37u6DDz5Ic7+F9mXMmDFu9OjRbtSoUe7SSy91W7dudeXLl4+qzbSQ0S2EEEIIIYQQCQSGa2gOMd5olnB51Tt37nT169dPyU1+7bXXzCv7yiuv2Ouzzz7b3XPPPWZUfv311+bhPvroo93zzz/vJk2alKYnONjI9QzP6dOnmxd7/fr17ocffjBPcZ8+fdyff/7pRowYYV71bdu22e/zGx6HDx82o33s2LEp6woVKmSPbBfGb9GiRSN6D2j7u+++s4kGvPrBsL27du2y/YJRHkq4vniefLaBkP3zzjsvqjbTQ0a3SDgIfclqrW7VUBdCCCGEEMnKwIED3YABA1Kt69evn+vfv39Yo7tUqVLmXd6xY4cZ302aNHFvvPGGeaUxFK+88kpbyPH2DHfeu+iii9zll18etg+PPfZY2HWEcX/xxRfulltucU2bNrV+YmDjmcYYZ7Jg0aJFqQxuwHN82mmnuWOOOcb5AaHsntFdt25d61MwrGOfsC9CCdcX9geec/Lf582bF3Zfp9dmesjoFgl5kRFCCCGEECK3Qkh1t27dUq0L5+X2jO5Qfv75Z3fVVVe5U0891R133HHuf//7n/v2228t5xtjNa3vRdL2fffdZ4Y8Ydh4ugm39jzrGPDbt28PK0y2c+dOmxzwixo1apjRjcgZz0ONbrzR/GY4wvWlZcuWNgkxdOhQe+3liEfaZnrI6BZCCCGEEEKIBCKtUPJw4On95ZdfTAQMby3e7AYNGpi4Wa1atSxv++233zZPuWdwA4b4Rx995NasWRO23Z9++sk+44EHG0Ob8lkPP/ywW7FihVu4cKFFqE6YMMHW413/8MMPLeR82rRprmLFiinfx/j3hMn8AEN78uTJZnTj3Q+F3/JC1EMJ1xdyxnH+jRs3zkLJq1Wr5po3b55ihGfUZnrI6BZCCCGEEEKIJAVBMYxscpS98HLA2G7fvr276aab3PDhw00ELJjQfGbynP/66y8Lrw7n6cagf/fddy2nu3Llypb3jAgbCyrgiI5t2bLFcsRZUDUP5pRTTnGbNm3ybbvPOOMMqy2O0c22hrJx40ZXrly5sN9Nqy9MWOzevdvNnDnT3XbbbWaIEz7vGdrptZkeKhkmhBBCCCGEEEnKunXrzAgOBS83YmAY5Pv27XMdOnRIeQ8PN0YzHnLPiOzdu7d5sD1Q80YUzYNw6zlz5rjq1au7AgUKmFf77rvvtrxohNm6dOlibUDr1q1TRN08ihUrZkYthqsfEMJOiDgl0TCig8EjjUFOX8ORVl/YvmbNmrk//vjDvN1lypQxIzySNtNDRrcQQgghhBBCJCmIltWrV8+e//vvv6new9u8cuVKe8SQ9Bg0aFCKOvlvv/1m61A3xwilDbzeV1xxhXnPCRsHQq9R98aYD14w6Fu1apVqXe3atS0/OpQ777zT6mH7GWIezggmJJwJg3B52en1hbxwIgVQSkedncgBTzU9kjbTQka3EEIIIYQQQiQh33zzjRnGCJi9//77Jr6Gkbhq1SrXpk0by3nmecGCBU1U7cYbb3SffvqpW7Jkibv66qtTQshZeH/+/PmmOn7ZZZeZB338+PEWKo7XGjVv8r/5TQx5b8FjPGPGjFTr+M2vvvrqiP526NDB2iV82w+YBMDwDmb58uWWw55W/fG0+oI4XKVKlew5YfmElV944YW2fZG2mRbK6RZCCCGEEEKIJKR48eLuhRdesDBoDGLysREx69mzp3m3ly5dauswnCnnhRFOODn1ujHOEUqrUKGClc/y8pbxdLNg0PJdvL+h9bozqimeFnny5HFTpkxxbdu2NZEyT+X8ueeeS/M74d4jpBwaNWqUKuccEIybOHGilSGOpi8I0Xkh+HjqUWH3iLTNNH8r4PVOpAmKfKF1n8OtE4nFzm+/9qWdbQWK+9KOELGg7F+/u0TEr/MmUbdPCCFyC22nfeYSkSnXNsjR47zalcpn6ft4rtMzlOPNfzHsX7RtR/L5rPY3cf8TQgghhBBCCCGiJpEN7lj3L9q2I/l8Vvur8HKRY0nUmUsh/GTM+l2+tdWsRhXf2hLZf51KxKiARNw+3RuEiI9HOVE98E1rnO5yoqdbJBaJPQUihBBCCCGEEEIkMfJ0RwAJ8+RwB7Nz58649UcIIYQQQgghRHIgozsCqEkXSqgRLoQQQgghhBBChKLwciGEEEIIIYQQIkbI6BZCCCGEEEKIXAa1rqlVLWKPjG4hhBBCCCGEECJGyOgWQgghhBBCCCFihIxuIYQQQgghhEgg/v77b7d3795UC+tCmTBhgmvYsGHK6z///NMqL/32229u9erV9l6RIkVcixYt3LZt2zL83cWLF7sKFSqkWkcIOqHojRo1crfeeqs78cQT3Q033GDPjz/+eDdjxgz73LJly1y9evXs96655hq3Z88eX/ZFTkDq5bmMXr16uUOHDrmczrBhw+LdBSGEEEIIITJdPWnAgAGp1vXr18/1798/1brLL7/c3X777WZsFy1a1C1YsMA1aNDAFShQwDVt2tTdeeed7q233nJPPfWUu/LKK80wPuqozPtdf/zxRzdu3Dj73eHDh9u6mTNnugsvvNA1b97cdenSxU2ZMsXddtttrnv37m7MmDGZ/q2chIzuXAYGtwxSIYQQQgghEttR1q1bt1Tr8uXLd8TnChcu7Bo3bmzGdps2bdzcuXPdtddea4YwXmgMdXj++efNQ/3ll1+6+vXrZ7pf7dq1c2eeeaY979Spk9u1a5d5wWfPnu2OOeYY+z084z169HA333xzpn8np6HwciGEEEIIIYRIIDCwMaiDl3BGN7Ru3drNmTPHnmN8E9r9008/uVNPPTXlM4SclylTxm3dujWqfhw8eDDVa9oJ95zQdULaixUrZh73tm3b2uvcEGEbCfJ0CyGEEEIIIUSS0qpVK/Mwf/vtt65cuXLu5JNPtkdCwT3IB9++fbsrX758um3hpf7vv/9SXi9fvjyiPpQtW9bVqVPHTZ482V4HAgHL6cb7LeTpFkIIIYQQQoik5YQTTnBnnHGG5YHj9QZyrvft22d54Vu2bHFdu3Z1lStXdnXr1k23Lbzhv/zyi30HL7cXnp4RLVu2NC864evkk7/99tuuWbNmZnwLGd1CCCGEEEIIkdSQxz1x4kQLLYdChQq5efPmufnz57vq1aubQTx9+vQMRdQqVark7rvvPnf++eeb8jnPI4GQclTMhw4d6k477TQ3depUe503rwKrQXtBCCGEEEIIIZKYO+64w5ZgMLY//fTTNL9DabBwnughQ4bY4uF9hnJioeuC1dTxoi9dujSLW5IzkadbCCGEEEIIIYSIETK6hRBCCCGEEEKIGKHwciGESGKa1ajiW1tzV611iUanyiV8a2tbgeIu0Sj71+8uJ5OI25eIfRIikUnEayc0rXG6L+3MX7XOJSK9r2ka7y4IH5GnWwghhBBCCCGEiBEyuoUQQgghhBBCiBgho1sIIYQQQgghhIgRMrqFEEIIIYQQIsnZt2+f++OPP1Kt++abb9xvv/3mEolAIOAGDx7s/vnnn2z93f3797vhw4e7eCAhtUySP39+98ADD7hkY+fOnfHughBCCCGEEMJnnnvuOTd37lz34YcfumOPPdbW9e7d25166qluxIgRqT47YcIE17FjR3fSSSe5v//+25UpU8Z99dVX7q677nKTJ092RYoUMbvh2WeftXXpUbFiRTdy5EjXtGlk4m+PP/6427Jli8ubN6/777//3L///uuOOeaYDL/3v//9z+XJkyfls/ze119/7QoVKuR+//13N3DgQNelSxez00488UT77E8//eT++usvW1egQAE3a9YsV6xYMXfjjTe67ERGdybhn5qMJONEgRBCCCGEECJ9Hn74YTd79mz39ttvuxtuuME83J9++qkbN27cEZ/Nly+fO+ecc9ySJUvcF1984Xr06GHrDx06ZIb6gw8+6C6//HIzVoPBO42xHAzGrGfkB3uzWY46KnVg9dq1a93UqVPd8uXL7fWKFSvcBRdcYIY0RjITABjhBQsWPKI9jO5u3bq5J598MmUbBg0a5Dp16mQTA/SVz9EGxjjtYXx723D00Ue7iRMnuvPOO8+1bNnSjO/sQuHlQgghhBBCCJHEvPLKK6527drul19+cU899ZQ766yzXN26dd3hw4dd48aN7fXBgwdTPo8xjJeZkGs8wZ4hvXr1anf66f+vHNuePXtS/Q6GLp7lokWL2sLr7777zl188cXmHWcdjxjiw4YNO6KfTz75pHm6PSO9Tp061i9+588//3R9+/Z11113nT0PXniffnoGt7cNGNhsgzcZQF8wpkuUKBF2G0444QQz0LM7zFxGtxBCCCGEEEIkMRimeKYJ28ZwZtm8ebM7cOCAhY1jjAZ7qDG4yfeuX7++eYqhf//+ZsA2b97cXuMJx+ONEUu+OOCF5jP83vbt280jjaFdvHhx99Zbb6UYyHjMu3fvnqqPhw8fdp988ol5mdMCAxzvdCSwDUwwsA3vvPOO9ev66693999/v3m5Mf7POOMMV6pUKXfzzTenfI8ogGnTprnsREa3EEIIIYQQQiQQeHD37t2bamFdWkSSEx38GQzWs88+24xzjGW44oor3Ouvv26/tWvXLsuPxrAmTP34448/or0pU6ZYqDb54Ndee62FbqfH1q1b3WmnnZZuX3/++WfLTWeCIHhp1KjREZ9lGwYMGGDb0LZtW/PAExp/xx13WP8x/jHy8f6/8cYbKd/DCMe4z04hNxndIsfQq1cvy1lX3roQQgghhEh2/Sg8tcFLeppSeJEzItjIxGANZdOmTa5Bgwbukksucc2aNXPnn3++Gaihiuiex/uZZ55x9913X4rBjoAbnva02Llzp7WXHuvWrbO8dPrqLUOHDnWFCxc+4rPhtgGBuCpVqlj/WfB033TTTUd8jvBzDPPsQka3yDEQxkLuSLj8ESGEEEIIIZLJmYSnNnhhXVrgDR4zZowZk6FL2bJlLfwbITIPPL1Lly619zFOoU2bNu6yyy6zcl6EpN9+++32umTJkmFzyFE+530gzJxQ9K5du6bZx+OOO85CwNOCUHgE0DD8g8HzTi52KGwDzja2AQ+91y/C4b/88kvbBvoYHFruQT/wjGcXMrqFEEIIIYQQIoFAoAzvbvDCunAQdk5eNqHgqJFTJqtz585ux44d5s1lQcU8WBGcElvt2rWz9ygz5oHIGIrgiJZhwHqq5sGQH07+N2HgwfCb/DbvheOUU04xb3paUKqM/Gy8+sGQJx5OaZxteO+992wbOnToYOtKly5tgnJvvvmm++CDD2w7rrnmmlTfw3tOjrqMbiGEEEIIIYQQGTJv3jwzLDF4mzRpYsrh5DEH19cmDPyee+6xklpeGHflypWPaKtVq1amCo63uVq1atZeMNu2bbPPoEAe+n2+N2nSJPfyyy+7hx56yELQgylWrJjlhm/cuPGI38U4RhTt3nvvPeK93bt3H+HpJrScdsJtA556vO546gmBDy1x9tFHH7mGDRu67ERGtxBCCCGEEEIkKXPmzLEQ8eB62aRblitXzgxfvLrUxqYeN7W8AS9wvXr17HmwcYwxy/dWrlxp+dfB4eB8H0Xz9u3bm9eakHWWNWvWuCuvvNKeI7728ccfu3fffTdsqPmdd97pnnjiiSPWo3TO71111VVHvIfHPrgEGOC5RzWdvoZuA0Jt7Au+F1onHCg7FjwhkR3I6BZCCCGEEEKIJIQ8aLzLeLoxNr28bfKnH330UXf00UeboTtixAg3Y8YMy3GeNWuWKZKjCI5yOV5rDFgMWUTU1q5dayHkeJLLly9vBiqGO/W78UY/9thjrkWLFvbbhHhXrFjRSnaRdz5//nz7HAZ6cE1tD8LA8bLPnDkzZR0K5AigkZcdbCTjwR83bpz7/PPPrc54MM8//7wZ+mwvue5MPDDhMGrUKAtRR8GcsmB4u2vWrOleffVV+95LL71k+yC7Pd2pfe1CCCGEEEIIIZICjNQXX3zRDF8MUIxtDGWvxJdXVxvDFu90t27dzKgePny45YjzfUK0b7nlFgs/x+ju16+fGbALFy40g37ZsmUp7XXs2DFsTnmwSBvg9Q5Hnjx5rNQYJb6oB75hwwbzivNblSpVSvVZ8rURQ6M/F1xwQar3WrdubYY4/STnm7B0POVMNGCoewJvbCuq7xjaTApg3AfnsGcXeQJeYL/IFaDwl1PVvUO37esNaZcsEEIcydxVa12i0aly6nCyrLCtQPgBQDwp+9fv8e6CEEIk3bXTz3vW/FXrXCKyuH+XbP9NQsvDhWPH+rcCgYAZ4363m5n3Y4XCy4UQQgghhBAil5OdxuhRQb/ll8Ed2m5m3o8VCi/PZeTPn988wjmRnTt3xrsLQogERl5lIYQQQsQDGd25DHIacio5dTJBCCGEEEIIkbwovFwIIYQQQgghhIgRMrqFEEIIIYQQIokJp42NSvfXX3+d4XdRHkeBPNH5+++/3ZAhQ7L9d7dv327lzLKCjG4hhBBCCCGESFIo63XhhRda+a1QvSPqVVM2LJRnnnnGjRw50p5TYotyXRUqVEhZypYtawJnlPYK/j51ugsVKmSfOfHEE610FzRr1sxes54yXpGU5cqbN6/V7I6Uu+++2x08eNCe06d//vknoskIJhWCP0sdcUqI0dcCBQpY3fIdO3bY9rKudOnS9r5HkSJF3AsvvOAWL17sMouMbiGEEEIIIYRIUq666ipXvXp1d/HFF7uHHnrIFS5c2JUrV8717NnTrVmzxp166qkpxmS1atXsO7Vr13Y9evRw3333nRnmP/30k5sxY4bVt161apU799xz3TXXXGN1rY8++uiU36K2N7+3efNmq/WNSDMcOnTIvfTSS7Yeo9Zb7/FvGMOfz2CghxrJlPUK5YMPPnBbtmyxOtwwffp0M4ZZihYtav2iPZ6HLnzmtddeS7UN48ePt75edNFF9j36X7BgQVvHfgjuP7XPmdjo2rWrO3z4cKb+RzK6hRBCCCGEECJJwUDE4MUT+8QTT7g///zTjGXW79q1y23dutVt3LjRwqS///57+w4G+sMPP5yqncqVK9tny5cvb4bmlClTzBsdWnILr/H+/fst3Jv3MUTxWGNse+zZsyfl+ebNm83QPf7441MMYYztAwcOmIfdW8dkAX1+9913j9jG/v37u6FDh6a8ZkKA7/M7bG+HDh3cgw8+aM+DF97/66+/3O23355qGzCy2QYmA9iG1atXp+o/bQdPFLBvGjdu7CZOnJip/5GMbiGEEEIIIYRIUt5++20zEPFoY8ziLaaqD4YqRuyzzz7rOnfufMT38BqfeeaZ7rHHHrNw6rp167pNmzZZCPeKFStczZo1zfhetmxZynfwQhM6Xr9+fffII4/YOgzaM844wz4PtENYesWKFe11hQoVzFDft2+fGcJ40gnrpq8nnHCCW7Jkia3fu3evGfLXXnttqn7++uuv9n6tWrXS3Af0mfD2SGAb7r33XtuGL774wv3888/urrvucvfff7+9f8opp1jfCKPv27dvyvduvPFGN3XqVJcZZHQLIYQQQgghRAKB8YkRGryEEzvDk92lSxfzwm7bts3W4fHFAL755pstn7lly5bmPfY8xZ999pm78847Xbdu3ew1nl4MTry98+bNM6OT5ywY0MEh4Bisl19+ub1HXrhnjL744ovWFxbWY8iuX78+7La9+uqrrn379u6YY44xAzsj77HXj/Tg95hoYFuCl1tuueWIz7INo0ePtnbPP/98V6ZMGctVb968ufWfcPpvvvnGjP3HH3885XvnnHNOSqRAtMjoFjkGZvI42VSvWwghhBBCJDMDBw5MyVn2FtaFUqJECbd27VrzVGO8Lly40A0bNszNnDnTwqUbNmxoBjW52nx/wYIFrmTJkq5BgwYmoAYYp08//bR5pPk8udOeoBqe4OCc7nD51hioeMwRU2PBQCYcO9xn9+7d61555RXzNMMNN9xg/WB9WiAIV6pUqXT3F+HtGNF41L2F3yBkPZRw/WKi4KyzzkrZBqIGevfuneozCK2Fy02PhNRB+kIkMeEuREIIIYQQQiQbvXr1SvFEe5AXHQ4McvKvMZbJx160aJEZqnisWXfJJZeYZxeRNVTJyZ/GoPZyuvE48xzhNfKv+bynhN6oUaMjwrgJZyfEHC86udXdu3d3Y8eOtTxyDH087xi2ofngQM45HuWqVavaa/qIAjqh6iiEh4MQd/Kv0wKPOn0hxD0YDHnaD4VtwMPOdvMZtpv+MzlBFAA56hj5RAqEguGdGeTpFkIIIYQQQogEAgMbL23wkpbRDUuXLnV16tSxEHRCoAknJ3eacHFyqTG68eRicIeC9zY9T3cwv//+u4WvE4Y9atSolPXkROMAYz0TAF5+dKgC+ZtvvumefPLJI3LLUSMPVhgPhokEhODSgjYJeQ81iMkDL1as2BGf/+OPP6x+OX1t0qSJrUOtHXE1xOhoj33FumCYyAjXXiTI0y2EEEIIIYQQSQzh0bfddpsZ5hiP5CljhLIQHh7O6+xBKHZanm7ymIONWcK4yeEOV0ObPPJLL73UXX/99amUwIHQ73bt2plhjXhaMEwoTJ482TVt2tQ8z/fdd1+q92vUqGGlz/B2I24WagiPGDHCwupD2b179xG/hYI7XnFKqoXy/PPPW6457xOeHwrrWrRo4TKDPN1CCCGEEEIIkaRg0M6fP9/qbgNq4xiq4SC02oNSWhitadWepob1t99+a3njgDH/+eefu3r16tnr4PxmnmPIrly50rzpGK7Bxur5559vYmWUJCtevLi1SVku6oXzmvButoHPeAJtHhj9N910U6qSYYBKe8eOHS1fnXZC2bFjR0rfPQi957OEloduA8a/V+qMfRMM+wij/NZbb3WZQUa3EEIIIYQQQiQhGI2EdmN8UjILg5t8axTLyavGMMVo9ZTP27Rp42bMmOG6du1qxi4h6ZTs4jOEpfM5PN4ooaNwTp42QmkYyGPGjDEPepUqVcy7zIKhOmfOHBNmowwYIe2EihOaTg45/atZs6aFt3fq1Mk84nisCe1GFZ3+4t3mfUp4ffXVV0d4uoG2CFvnfc87T6kyfg/ldA/Wz5o1y+qWk+uNwJsH+2P48OHuyiuvNGVyvPtffvml7SMMfULNySsn1B6PNtvEvoI+ffpYCLtXBi1aFF4uhBBCCCGEEEkIxjACYhjKn3zyiRmUGJYY4BjQeKd5jlGN4Bo5yT/88IMZqxilnjAaquaTJk0yzzdeXkTcPJVvRMUIN8fDjRAbYEyT/436OMsdd9xhxvJRRx1lhjH53uRUE9perly5I8K5MXTxhgd7xCGc8BkULFjQ+ofxjZFPDju/Qw52cAg5YfSooxNaTih78O/SZzzViL/hAcfrPmjQIAs5Hz9+vE1WeKXJMMAHDBhg32c/sb8w5jNLngBbLEQO5OsNW+LdBSGSirmr1rpEo1Pl1GFhQgghspdtBYq7nHzPmr9qnUtEFvfvEvV38PKuWLEiw5rWQBg1RrgfYJxjbGcH/wX9lufF97vdzLyfEQovF0IIIYQQQogkBy9vJAY3+GVwQ3YZ3KG/5ZfBHdpuZt7PsP0sfVsIIYQQQgghhBBpQ3i5ELmRQ4cOBfr162ePidJWIvbJz7YSsU9+tpWIffKzLfUp+9tKxD752VYi9snPthKxT362lYh98rMt9Sn720rEPvnZlp99EsmFcrpFrgWlREJryGlBeTER2krEPvnZViL2yc+2ErFPfralPmV/W4nYJz/bSsQ++dlWIvbJz7YSsU9+tqU+ZX9bidgnP9vys08iuVB4uRBCCCGEEEIIESNkdAshhBBCCCGEEDFCRrcQQgghhBBCCBEjZHSLXEu+fPlcv3797DFR2krEPvnZViL2yc+2ErFPfralPmV/W4nYJz/bSsQ++dlWIvbJz7YSsU9+tqU+ZX9bidgnP9vys08iuZCQmhBCCCGEEEIIESPk6RZCCCGEEEIIIWKEjG4hhBBCCCGEECJGyOgWQgghhBBCCCFihIxuIYQQQgghhBAiRsjoFkIIIYQQQgghYkTeWDUshBAe9913n6tdu7YtZ555pjvqqMzP9/3vf/9zv/76qwstvFCuXDmXk/jvv//c77//7kqUKOHy5MkT7+6IOLJr1y47DkTysG/fPnf88cfHuxtC5HoYMxxzzDE2ZsjK2EOIrKKjT4hM8t5777kKFSq4o48+OmXhgs5jtBw+fNg99dRTrl69eq5MmTLuu+++c+eee67buHFjXPvlF9SjpF/XX3+9GQ9s21133eVGjRoVVTsvvviiK1y4sG1f8HLqqadmql+fffaZ69mzp2vfvr3bsmWLe+ihh9yhQ4fius+ZULjqqqtc/vz53UknnWSP1113ndu5c2fUbeVE5s+fb4/Bky4//vijnUMHDx60Yys724nlQLFbt26uUKFCrlSpUvbYo0cPWy9iA9cBzr9gVq1a5c4777yo2ypZsqS7+uqr3aRJk+x4yiwdO3Z0c+fOPcKgj2aS8aOPPnI33XSTq1atmitatKhdj5kQOOOMM2xC9LfffnNZhfMmqyxcuNC9/PLLbubMme6vv/5yicbgwYPd3r17XTIzefLkNI/HIUOG2L1nxYoVLtnhHLnjjjvs2lmwYEH37bffurJly7rly5fHu2siN0OdbiFE9Jx00kmBnj17BtatWxfYvHlzqiVaOnXqFKhZs2bg5ZdfDhx//PGBTZs2Be67777AxRdfHNd+eXTt2jUwbty4wKpVqwL//vtv1N8/dOhQYOXKlYFJkyYF+vbtGzjnnHMCRYsWDZx77rlRtXPCCScERowYEfjf//4XyCrPP/984Ljjjgu0a9cuUKBAAdtfF110UaBjx45x3efNmzcPXHnllYEVK1YEdu7cGfjqq68CrVq1CrRo0SLqtnIiJ598cuDrr78ONGzY0I6Df/75J3D22WcHJk+ebM9r1KiRre3Eii5dugTq168f+PTTTwM7duywR17fe++9ce1XTubmm28OFCtWLDBs2LDAnj17At27dw8UKVIk8NRTT0Xd1vr16wPPPPNM4Pzzz7dreps2bQJvv/124K+//oqqnTx58gROPPHEQK9evVKuvX/++aetjwS2pU6dOoHp06cHfvnll8CBAwesHdr44IMP7HpTsWJFWx8Jl156aeDgwYMp/bj77rutf0cddZSdU48//njU94j//vvPrnFc3+vVqxcoU6ZMoFSpUoGlS5dG3MbixYsDN954Y6Bq1ar2Pzv22GMDhQoVClSpUsXuX1xLI+Gjjz5Kc+H/yPWB59nZJ7/gmC5btmzgkUceCfv+e++9F7jqqqts/yc71157rR2rc+bMCRQuXNjuxYMHD7bjS4h4IaNb5AoqVKgQOPXUUzNcooFBwcaNG33pHwbohg0bUp5v2bIlsH37drtBR4uf/fJ48MEH7WZ85pln2qC0bt26gTvvvDPwyiuvRPR9BmQXXHBB4Omnnw58/vnngV27dmWqH+XLlw98//33AT8oXbq0GTLB+5zJDrYvnvucwVmosf7jjz/aBEE0MPD99ddfIx5MZ1dbWeWss84yI7lt27aBq6++2o5BJk48MJyzs51gfvvtt8CHH35oA/PZs2cHvv3220BmwZBZu3ZtqnVr1qyx9fHCz+3z87jy8/hcvXq1TQrmy5cv0KRJk8DPP/+c5Tbp23PPPWfXFgw3DLEffvghou9iXPN/b9SoUeDCCy80wxljl2tqJHC8ePeWcHAOYOzOnTs3ovb4XYw34HzBiPn444+tj6+++qpNQD7xxBMZtoPRuXfvXnu+YMGCQO3atQP79u1LMcIfeuihiCe+/JxYqF69um3jaaedFmjcuLHtd2/JmzevTXyxPjv75Oe5x/0XI5TxRTjoI9sZKX71y+9rC/fRrVu3prq/cx5mZkyVSPc/kdwop1vkCsaNG+d7m0888YS799573RtvvOGKFy+epbZOOeUU9/HHH7uKFSvaa3J4CTHPTNi0n/3yePzxx92aNWtsoV9z5syxMDXC0Ajhyoh33nnH/fDDD+777793CxYssHxlwr6qVKni+vXrF3E/hg8fbr9HCCK54VmBHK/Q8O8DBw5kKg/Tz33eqFEjN2XKFPfggw+mrCNEtWnTphl+d8+ePe6FF15w06ZNs/BM+rJ//35bGjZsaCH9DRo0iKgffrb177//uokTJ9p2cQzw/+cYL1asmGvRooW79dZbIz7WCxQoYP+7t956y8JuL7roIgu9HjRokKUKhOb6x7odWLJkiR3H5ODXrVvXnXjiibafSA9hufnmmy00/Nhjj424zRNOOMGtXbvWnX766SnreM36SCB0OJJQ9E2bNmXr9vl1XPl5fHr89NNP7plnnnHbt293d999t53PXHMeeeQRd9xxx7looS+zZs2y6x/XTK53pIr8/fff7vLLL3cbNmzIsA3Ok5NPPtktWrTI+nH22WdbnyKFdB6uH3369An7/ieffGL95HiJhODzgjDwpUuXplyL2T7O6c6dO6f5ex47duywY5vzjevwlVdeaeeft81du3Z1I0aMiKhPpGZ9/vnnKfdPjyJFirjGjRvb8UCqDtt62WWXpdvW119/7Z5++mk3dOhQO4Y4rj1NDbaN+14kof1+9cnva0v9+vXtetu/f3/3yiuvHPE+aVGRaIj41a9YXDuB45lxX9++fW17WBhjRTpu8Ov64ue9T+QA4m31C5FMMNvNLLc3A16iRIlAwYIFbUbbWx/JLHgohEDhWWEWnRC01q1bm/d03rx5meqjX/3yy1PNDPaoUaMCPXr0sNDp008/PVCuXDnznkcbsYCniP4UL14801EKQKgZHh7CwvEiDxkyxDxchITG81gg5B7vFvunQYMGFurH6/POOy/dNsePH29hi3jUmJUPhnDpRYsWmeeuffv2Gc7Y+9kWoZh4rO6///7AsmXLUr1HeOXYsWPt/T59+kQUlkqUBdx1113mhZwwYYJ5Zl588UVbX6tWrQzb8Kudv//+21JDLrnkEksHCMf+/fsDAwYMsG2MJkpj6tSpdlwSUcJ5xyNemmnTpkX0faI4uIbgdSTMNa0lO7fPr+PKz+MzGPY3aT2eJxfv2OWXX25h09HSsmVLS1shYoLw9OBIGELPI/W4ce57/QG8gFxrIvV08z/G61etWrVA586d7To3fPjwwKOPPmr/V65ZkUYsef3x/s+EKod6JJcsWWLX6EhgW7imc43j/hKcNvTOO+9YylUkEL6dnned44H7ajTpPqQKcQxxT/C20fOYZkef/D73+Cz7l2ss6SrcP/EE8zp4Ybvz58+fZjt+9SuW10748ssv7Twhos2LUOA56VoZ4df1xe97n0h+LCko3oa/EMnC66+/HtHnOnToEHXbeD2YDd22bZsJfrRr186ddtppvvYxM/2C6dOnm6caT/fPP/8ctacaAR88NMFLpB67UEGgtMBTGS14ssaPH5+yz5lVv/HGG+N6LNBusKfBu0Szznse2iYeodmzZ7sJEybYDHp6vPTSS+b5YtY/HH625XkKmemvVKlSmu38888/rnfv3m716tXu/fffT/NziOAgLEWERa9evSxq4rXXXjNvdZMmTcxz1qpVK/PAIYhXtWrVmLYzbNgwi47AA5kR/OaAAQPcjBkzXKQsW7bMjR071jyweNc6derk6tSpE/H3+c1rr73WPG54S6PFz+3z67jy8/gMBdG0GjVqHLEebxf7Mdrol7Zt26aKVMgMRDdwnQ2G6xWe70ivLajfc2x/8803JgSWN29eu/7iWeQ4jyY6h897YlSct7Vq1XLz5s2z159++ql74IEHbB+OGTMmovYQ9cLrilcRz+8ll1xinkWOI64bRAREcl/AU44IKV5kPIeIxSEQh1gmC8dyJFFZoXBd4NrEuce1jGMkEk93Vvvk97Ul1IMdfF8JpWXLlmm25Ve/Yn3t9LzVXCs4X4gmxLNMpEF6+HV9iebeR1QIQm/p3ftEzkBGt8iVEPLHzR1l6MyCkjYGWriLOCFIXqhcPCG8lAEWZKVUxuLFi9369evdunXr7BEDHGOEEmDvvvtuRG1wqcGI2Lp1qyl9n3POOXEtPeYnfh4L3uAo3KCIULVwMHghXDrS/cHAGxX4WLf1yy+/2D5BPTbSiae0BiikNXDMMHhiXxPOz2AdQwLjBuV5wsW9/c2xz7bEqp3sgAE+Bj/9ySwYNXw/K234gV/HlZ/HZ7TccMMNZhz6Ub7Nr7b87FM0EIL/5Zdf2kQs6RjARHH16tXdww8/nHLfiRTuC0w4ehOgbdq0iTjk3e+JhVAwlO+//34zoDZv3hyxcjzfo0+cx373ifN69+7dpkYfSeoD+xWjnzBt7i1U6+Aa6KVY0VcUvytXrpylsUu0/YpVO4zxmDQNp6r/6quvpvk9v64vft77RA4i3q52IeIBgjaRhomlxRlnnGGh1ihyo+rtQbgqIXuE8EUKQjc33HBD4PDhw6aoTFgdomWE6kUL4jS33357oGTJkoGjjz468M0331h4ZCRhVemFytE/Qq8XLlwY+P3336P6PuG7hOkh4MJ+45HX0YoUZVXQLTS8jZB3wroIlyeslPBLRG/ieSwEg0owIkWEss2YMSOQVQhlRKBo27ZtCdUWIXwvvfSShROmBeJKu3fvNqEjxM8Iy+Oxf//+gUqVKpnyfyRh4X61ExpyS7hvWov3fjyuU37g5/YRnukJHHnMnz8/5TnhrRmFvfvZTjREE16cXW352Se/uf76600gK1HaSTb++OOPI45xIE2LaxXVNgiDvuOOO6Jql5Boztfg+zj3UMTjOK+yq1+x2j4gxYFKJFzXQ5do6NChQ5rvvfbaa5YaGAmkc6TFzJkzbZwmcj4yukWuxI+BygMPPGDGI7m25A5xowCMLHKYyVuOFMrKYChjEJAfTI4PJWIyU7YoVqUy6BslXMg1Dc1PiiS/EeOY0mHAIzfSaMtg+VV6DK677jr7PkY3gzr2GUvTpk3jeiykNUgiVzxSyN2jL8G5oIBqLscBBmc82mJSiX0dalyT23nKKadY+a5Ic7HJlyNXdcqUKXZseYOjaHO6s9qO1xY5uuQOsq9oh9zwTz75JEvl4/wyqJhU6t27tw2oUehHV4FzMtI8ST+376effrJcZw9ynTl3OLfJq+c551N2tRMNMrpz/jaS74y6PBO7TPCG6qpw7YpkgokJc/Q3uIdj6HHuMWmMjgiK8kzwZlQ+jklX8n779euXqh/k5z/77LN2DcZYJlc5GrjnMVEWnI/MOq7lo0ePzvD7fvUrVtsHt956q12jmFyPFu873PO4xqA4f8UVV1jJv+Dca5wjTDxnBN9hLIA6PyUfQx0gaLcw/hM5HxndIlfix00cIRLPq8mMMUJoDASPOeYYe41ISqRwQcZjyE2QvnGRpqQH6+NZKsMvT3VwnzzoF+ujwa/SY95MOBEGDIYYCBEhwP8v2tJcfh8L4WDfIVqUkUHLRIIHg6pwnmO2N6NBo99tMdj0Jm74vFcaKHQAFslgluPOGxy/++675tFkYOZN6ERa6suvdkJLu82aNSswaNAgq/nMRAnHBhNplEiKl7Fx2WWXmSAY1xUemVhgwYDO7u3jPAs+75mgoiY55yLHOJNUkVxX/GonpxvdXNtiEYXhR9+yu51I8Eqz4cVEZJNoEwwur6Y218GMaqRzH2e/Y2wTocT5h/HNwnGJQc7kLpNXkfLZZ59ZeyNGjLDoJ66pCJFx3BMdFy38z4OF67w63ZTljMZQ9atffm8fkXmhETqRHuve53hkAoBxBpPplJnEA89+ohQckydETaUFEYaIvPI5rkm0w7iDayZe/OXLl9u2MjbzI2JMJD4qGSZEJkGYCMEN8n8QKuKR/B7yeBCC4XWkkONMKRLyR8n3Ip/oww8/dOXLl4+6X1ktlREOxF4QcKK8B3lhlLyhpMvtt99u+yAjyPMjXy9YNIXXZ511VlT98Kv0GFAajLI15LZR1oXXiJlkJLQS62MBwZ3gPG62kfww8tnTg3xBT9DII5xITmiZtFi3xWdGjRrlBg8enJKrHi5fLtJ9hDgOuYeU2bn66qtNQI+yQ+eff74d45HKlPjVjgc5keR7esuPP/5oea/kTZIDmplz2S84bzlnEO3h+a+//mo5sORvZvf28b/3/v9oRZAnS84rua5oRnAO0hZlggYOHBjzdnI6lPRCGOvOO++MWgwuWSA3+s8//0zzfc5lrj1paWIEgxgcx3fp0qXtNaUbKYFWs2ZNu0ejJ5JRSS10I7gnePckxBlpjzzjCy64wNZRrot7KuUv0wPBPD77xx9/WO7wxRdfbAKQ5Dm3bt3anXvuuS7S3H/u3V7f2SeIwwVfw7nXIJRKuTN0LrKjX361EwrjA+7JiLhGC9c0SscB93DuFYzR0KT54IMPbDzENRSBRXLO04J9PXXq1JQ2gbEKYwSE1jyRQATZENwTOR8Z3UJkEtQtqdVIDWsEMJo1a2a1lLm4cjOLRijs+eefdzfddJOJbqCmjZF82223mQhLtKCYiUonN3OMCsShEKmJVvkzGAbqDGK5aQOPKG6GU/oNx8iRI824YXswKhmw07dQwy4juAEywKKWJ4MsDHAESDBso4XBODWxuTFy02fAfs0119j/NJ7HQmhNefrHDTm03mskoIYfWt+UfZYZUa3MtuVN/ATzxRdfHCFiiFEYiRHPOUJ7KAN7g1oUk5mUQP0Vgb9I8KsdKFmypNWZpf/8rzkvEJViAixaQalgIqmXG2n/ML6YLGOii/8j5zMTVvHYPoyf5557zmohv/feeymVDJh4YZCNOCPXv4yMZb/aye7/h59tZdQO1yNqhCPchdEdz8mfZJhYQJAOxXjP6OaYYkKHSeIrrrjChBYzgn2M8cqkLorsGJCoXDOh54HgWyQTvJxnKMFj9NMe9ywmwBA74//KtYv/MeOH9ECQkWuuN1FFPfpwsP9C1fJj2S+/2gkFg5ZtpP3Qew1VSiI5r4IrrHjnGYJ0jF0Yh2CYc59PC8ZJwdUmuIbSDtdM7ufcOzHo/byuiAQn3q52IeJB7dq1LXzbb9asWWMhtOQpkTuZWQi1JXwys5CDRK1JwrDfeuste50VyLV9/PHHU6177LHHLBc9UgjLmzhxouWXk5OdnmhWrATdwv2/CJkFjodgIaZEORaigVBID0LjCGFs1qxZqoVc3kiE3WLZFikKoQs5lF4YerQQDklYOI/RCuX40Q4hmdQbTm/JTC15QhrDCQ1FCznOpDiQOoFoD9oM5CqOHDky27fPy4lF0yE4/JvrHfmRpGXwmeBjJpbt5PTw8njilxBgJO2Q0881JDPaCcGQYnLSSSdZ+HAoaJkQMhxJiPKwYcPss5xvoQwdOtTCjLkfZgT3Xe/84vGee+454jOZSbPi3pQV/OpXrLaPNIFwC3n2GUF6GPcpwsCptU4KEqks9BHNHFJYOM74/y5YsCDNdmiD0HHGm2+++aalKCBwy7iJ1Bw0MdCo4TpPqLnI+ahkmBAJBCVY8EozU06YeVYgVIyZVWbuszqTSv1kPNWEf4V6qrMStp4Z/Cg9FgqhttldcicWUFeUMiuAR4OyV5GWLEmWtsIdDxyPmalpn5tgn+NhwevD/2Lnzp0RebT8hJQXQtopv0Q4KRDKyvWJ85n0GjxbeJGIEvE+E6t2QiGsmP3CMUUUANeXYKiXTtRJJOWE/GrLzz5ld5lLzns8upGW2Ip1O5FCiDmlMW+55ZYj3sMLi+eb9KqM2Lhxo0WXhIY4EzXEuYd3N737dzQlMaP9PBEv3NfDQfTP9OnTY9qvWG8f14G0iPQ4Yv8Qlk9UH1F6RMSRNuBB2tSQIUMsnSUchKATMUVU0d13320RN6RZUbIseNyCR55oOcLqRQ4n3la/ENkNokl4brJLTCYSENFg5pPZ76yU1AJmZVEnRsSLbcLDRUkkxNSygh+e6qziV+kxwJuJujFidewnvIDdu3fPlNppooDXy4Njmv8T3nvPm58obSGCh3CZJ1qWFYgKiVbdFtVghGzwNNAHKgXg2R4wYECqhWiO9DwZHnhAwonDZQVEjp588klT5mf7Vq9ebSrieFn8JqOSTH5sH5611q1bm5I/pQwRSSJapUyZMuY1ClYFpi9peaj9aicY9nOpUqVS7gXewn5/4YUXotpOv9rys0+xLm2YFkRpBP8/4t2O3+XHYlkSjfssx3YkJbzwpmdU+QEBty+++MJE3Ii84vM//PCDVW1AlRulcITjuJ4iVpoWfvXL7+2LpsxhRqB6ToTa66+/bs95DLcwHlq0aFGa7VCVg/epkEHEIY/hFkob8r7I+cjoFrkOQse4uTDoDy53k9WwtKzgV0ktaN68uRndK1assHAmylO0atUqU20lGn7uJ5SO69evH/j0009tooJHXlPSI1kJDeOmBAxqq0zAVKxYMfDcc89FNMjxu61Qo5vJEiaDCOO7+OKLTQ02Wi655BJTGSZcMH/+/PbYpk2biI3u559/3hRlGdBRn5ZrAqF/3uPDDz8caN++vYUVZjTgZ5uykg4SDgakqB1TN5zJOAbK1JJnf/lNRiHKfm4fxjshl9TQ5XgKLhXEdqL0602mZUc7pODwPx41apTtAyY7UNxnIpSBNyrUkZRR8rMtP/sUy9KGiTiBnZNSBJj44xpAqHu4ay3XBEpjYZBmNPHMb7AwuU+7VDHgGshEHscThj8TfEw6c21ND7/65ef2pQcTCaiEN2nSxNTkM4Lrx913323XYELMee5NTPHIQokvylwyeZAWpPAwHqMUKecdz0nL4r7npWjxHv8XrvMi5yOjW+Q68B6Q/5UZGEx49YrDzaRmdsDhV0ktr63QCQS8k5kphZUV0ptpzuy+8nM/MShfu3btEXnYrI/nsZAVQo1brw4rj+T1MqimJmgkufB+thVqwNMGHkv2/1NPPWUGAAZzJJ5vr3wMnk08NNSN55zmebR50/z/mJhiMASUhAl+ZLBGvzLy8rJNGOgMENNbooH973m1vUE5ugNZKf2XFaPbr+0LzrNGm4EJD09zAo8PpYPIcwytCx+rdipXrhx444030nwfDxRlgyLBr7b87FMsSxsm4gR2TjK6vXsS5fk4vjGGOQ+ZVOcaxfV33LhxEf0G+cx4sPF2E9VDGTSMbiY8mWx+5JFHUkoIZmR0+9kvv9qJBO47TDpkBLnl3jXGuxcA96lgOPbD5aCHtsP9I7120KBAq0bkfKReLnIdTzzxhLv33nutZAM5f9GwadMmy2sG8kj9wq+SWoD6JzljlDrxQDk1PZXNcJA/lV4ueEZlWPzcP7HYTyiTolJ7+umnp6zjdbBiaTyOhcxCHmuwGrVXngvIn77++utNwRgFXnL2PvroozRz5PxuK1hd3muLR/Z9r169LHcSxXfy5yjDkh4oAD/55JOm/OopFaMozHMU6SOB/7GnaEtfzj777JS+BYMKNudSJHAseOr+fkAuMlUMPNV6+kYpIjQV4oHf2wf8v8hjRDWePEdKkJHrGE0ps6y2Q8nASy+9NN3r6a233hpRP/xqy88+xaq0oXfuUDYyM3oKfIeKEeSRh5ZJDL3O5mbI/eYeR7k/8pTJ70cNnetDeuWq0iLcfg5XYSK7+uX39qUHGjfkWWcE+gavvfaa5WNTJYXnHOvcyyhPiJp62bJlrVpJetUb+D6lXzm3Dh48aM9ph21Fq4FtpCJE9+7dc4SmjMgYCamJXEHjxo1T1adEIIOLIGU0PMMJqMEYD/wUKqtXr54JjXFB58bAjYzyJeedd17KoDmS7WTgFwnZWYImdD8xIPP2U7SG99tvv23GHsJCDABpC7EUBgCUDktmGBxgVCIQFzqJgBAQwj5sayQTMdnVFsc8paj4v/A/TgsmA7ya8RgQnM+UhWFiiUdq6XqiU2kJhXE+cC4w+OE60KVLFysTxu9jCHiPkcKEA4Oz4GtJVpk7d6676qqrbEKA0jRMcnzyySc2WRjtBFpWhar83D4GvZy77HdgkM0gduHChSaGRt1eDGYm1qjZG+t22DbKTaVV+o5jltJFkdR49qstP/sUDIJOlDZcuXKl7SvOXY4pSsJxvhw+fNj2X6RQ6omybJmZwGaijprVbCvP0+Kiiy5yfuKnKFsyCcUxBuJ4QfiLY2f58uXukksusYk8/udcK5l0xgAcPXp00ot6hU7kIMT2yy+/WElIxMzSg+urV16N73kL1xoMdwTy+J8x4cy9I61a4Ewqp9UO4o6cb9R9px0m0SiFKnI28nSLXEE4FVI/YPDD4C907iramycGIwqYM2fONDXaDh06WJ1JZnyj5Z577rELO4NQZlHpG7Oo0Sh/QiLWcw3eT9TL5P/KfsqMEnbr1q1tG8eOHWuDPv5nzGIH19VMVqjDzACDgUEo3Nip2c4kTDRthYsAiLYtjAg8bOHUkxkkMeGRnsGNV45zAgOZQX7nzp3t+GYAw3MGRBz/GNMMjBhQhvN2ovY8a9YsG4BhrGOE4GXn3At+9EBZNj0wDLJSjzsceP6ZZMLTjuHNBNrTTz8dE5X2jLxcfm4fx1NwFA4G37Bhw2yig1rbDEhRBc6oTrpf7Xg12tMj0koSfrXlZ59CI4VQ5g7Gi/bBm4dhFg1MtnGMcu2MdgI72Jj227DOabXWYwnjBM+ji9I6k588Jjvjxo07Yh9z3fcih9Jj/vz56b7PPYdJ3xdeeMGO/7SM7rRUzT24rzLhMWLECLsnUmde5Gzk6Ra5Dr/KptAOs8LMVgafRumFXGcHePGYOeWCz02GUEsGVZQAKV26tEtEGjZsaB7Ok046KeIwdy9E2SOe+9zPCRgRHgYnhLLyPycqgf3qlYXxIhW8KBHAy4khHi4Ml5JZF154oZVqwQtKSDue79CJKf6XhAYSTp+Tye6STKEwaRJpWkcsYJJy6dKl5pXyUg2Y7GCig8ghJi+zuy0/+xSr6xSTZGnBxHEiIk93ak83E8+DBg2y50SHfPbZZ5basGDBgpj1RaSGc5H/jd8TtyIBiXdSuRDZjV9lU1D8HDFihCnLJoPCN+sTlfSEZILFeSjhhMLq+++/byJclLA6//zzTWk6nnAcIEoUWt4nniq+KG6nJ/KSHiglI6zjB6jSjhkzJsvt7N6924RrEHuijEuw0JsnoIaSsgcCa+kp3nLeor4MiLrB8uXL7ZiCaMQWY1EyDBE1xHX4XyAeV7NmzcCZZ55pJX/8JqOSTLHYPqBkEduJGj594Hk0KsXjx48P3HjjjYHOnTubGBFqyEOGDAmMHDnShMhQLM6Ib7/91tTPERnj+uKBqjIqxuXKlQt89913EfXHr7b87FO461TwNSre16nsxs/yY9lREs1T5feuUZ6qN9cvhMFQH49GSG3p0qWphNRQDkdArW/fvlEJqfnVr0TEr/ufX/c+kXOQ0S1yHX6VTWGA//333wcSDT8VvrOLSFVgURYPNYZ4HW2dZr9J1AkYSmllBraD88EP/GwLUKXm/920adOUSSVKf6E0jop5NP3iOgAnn3yylRGj7ir1kSmbU7t27cDjjz8eUVuxKBnGZBJlaRjUMmDu06eP1RPH8IqWrJZ28nP7GJh7YEBS0gtFdh5RI2diJVIYGFMy7KWXXrIyW7ymrBrrUJ1H4R4DIz3q1atn3w1n9DD4ZkLPM0Yywq+2/OxTLK9Tc+bMCfzxxx++TzBxjGd2gikRy5hlpU9cm9gnlM3i/4Yhd/bZZwcmT55szyO9HqA0z/WRUmGcE0zCU/aRR34DBXFUzPmNSIxuv/qViPh1z/L73ieSH8UyiFwHoi/k45DfRlgjIdfkdhImTj4gqpKRMHz4cHfHHXe4l19+OWqxMz/UwbND4TvRICWAvOvgvFZyFD0l6mgE9dIjWkG9448/3jVp0iRN4aNYQ36yJ4ITTKT7JRS2I3hb2DbyNQkBJhSSR3QCOKZQE4+mrY4dO4Zti3OI0NmMIH+cPHLCJMnd5jzq1KmT5SR64eYZgco1AjZeOB/9Yx0h6qSYoBdAqCe5teSO33333Rm2yWfIM04PlLUjBSEjROEIbyfnnNBPwoKff/75iNsI7hv6B+z7jPoY6+0jnJ88+9tuu83VqlXLFNobNGhgYa2o4Q8cODCqfnH8sV3hIKXmscceSzdHmpxMcvbDaV5wfJBnSc5lJPjVlp99iuV1ClE2RLfSU1qPBELRyYslZL5bt252HHD/Q5+BsOvsPtb9Jit94vrDdZZxynXXXWdaD4ietW3b1t6PNM2AEHJSaBhPcK3lesK1lOsLqTYIr3J9QeSS62p29cuD3+W4J8ecFD+uv9GOWfwaU/l1//P73ieSHxndIteRlbIpoYqY3Cgw1LmABovIRFLmJFZlpkaOHGliVIjchCqhJzsIJaFQ/dxzz9lNmXz1NWvW2LbGU1AvqxMwWQWDhf0QanRnRaAneMCPwvHQoUPtPMG45fjmN3v37u369+9vJfgibevNN9+0z2MgoxBOfvWOHTvchg0bTPwmo/xpzllUvRFJ84xmzj90C2iX8mEZlZphgEOZKQSDOIbIn0WYioEi5zQ5s+Dl0sajpFaFChXM6CYHl76xDyk5kxmBw6yUdvJ7+7i+kmfPRCDHLIP09evX2yMq7Txy3DZv3jzs+cr3xo8fbwNzvo8hybGIqB/7DCFEFtpAoTujCRNyWZlwQVAxtGzPgQMHzGDPaGLJ77b87FMsr1Ndu3a1azGTmVnJR01rgom2s/tYj0Ups6z0iZzP3RsAADssSURBVPMFw417HBUNEJ2jbxjRPXv2PCI3P9Ice6675557ri0e3rWXicfs6hcOEI5nRDC5znEe8/sIYbLcfPPNdq2OZLLCzzGVX/c/P+99IgcQb1e7ENkNedwNGjSwsB9CoNq1a2dhiIRGNWvWLHDxxRen+d3FixdHtMSb/fv3W6js4MGDLXed14kMYaWRhJfDr7/+Gnj99dcDgwYNCrz66quBbdu2BeJBhQoVLNfVWwiVJVyQPMzg9dnB6tWrA1WrVg27XzNLcM70McccE/Yzr732muXYR9NW3rx5w36GPG1yDtNj165dtk38/0Mh1ePaa6+1ENpXXnklwz5xTtx00032f3zqqacstHXKlCkW1hsthIru2bMn4CcLFiywkNDTTjvNQuo/+ugjSxeYNm1a1G2NHj060KJFC9t/mcGP7SP8n9BW75wgR7x69ep2vaxWrZo9EjLN46xZsyz0P1wY9Jdffhl488037bp2zTXXBDp16hR4+eWXLRWgffv2dh5wPSdNIJIw6t9//91CbDkuSS0iXPaiiy6yHHr2N/eF0HSdWLflZ5+C4Vj38zo1btw4+59xHyXEn/PSW6KB///QoUMDAwcODFx66aW27q233gp7TYv1sc7x54X1+3WPz0qfvNxoNGfQO5gwYYIdF1wvoVatWoHMQCpdVshqv0jh4dwllH3FihVpXqPRaeD4yu5UPr/uf37d+0TOQJ5ukevIStmU7CptQogVJXAyC2WV8AgnGsx+40kkNC14BjiaIgrsF2a/E60kSTzBa8V+JUXCC+vz9un/r92R8llC6/CeZqYcXSiEFkZbYi0tzxEe3Iw81Bw3eFVCvX9AySIU8FHdjaSeNGXm8FLgWfPqsrdp08ZlhliUDMPbSUkZD7yAXBfw7EZLVko7+bl9bBPl2KiugCeXY5C28WLx6L0G6pJT9iu0ygTeMK9cFqHHeCPxIgbz/fffW9QHxwPlw9JThSbEk/QMvGpEURD5wHnEsUj4O6HvkUaM+NWWn32K5TWL9oh+YEFZ3YO+RXONJmXipptusnOSKAZSDkg/iDSCye9jPdzzrJDZPqEwTjQOkTh8h/KYjFE4d7yKDRwXeHgp+0XbkdKoUaNMb48f/aL6C9d80hPSguvBo48+ajXrH374YUsrSiSivf9l5d4ncgYqGSZEAsAgkTJmvXr1soE/EwPc0N59912bDEh2MAgp38QAmHwxDENu1Nx4CffP6aRXEs0vyJUl3z0jIs1rY+CP5gFgFGGoZ5ZI2qI/keQBEoZYqlSpiPIOY90O52i4vFu/Pg9ffvmlhSMyMMtMbeaslHaKxfZt2bLFJjoI+yZ/l3DLLl262OCb+ustWrSI+LfIIWcSjjBz0goAQ500FAwDavKS18370e53wHAg/JW6vdHmmseqLT/a8eOYiiVMMHFeZmaCKRHLmGWmT6TPUPaQXGvGBpwnTBIy0UQKBsc74d1emVOuqaQexJpE7Zffx7pf9z8/730i+ZHRLUQCcMEFF9iNjJsXnh1uUo8//rjdMKIV9UpEMLAx9Ng+ZvupJY7AEYMraobGA/Kz6A9eNzwaDPbJCR08eHDEtdoTqQ4rniGEcTxvLZd2tgcjJ3TQjpeCGu7pgfeKaAly7DBi+D/RfmYGwuT/4anAU42hxcAts5NJ6BPQJzwoQB4cgxlv0MIjmgatW7eOeTvU+V63bp3lyGY0aMJz+fTTT1t+YCRwjnBs0j7/K7yd7DOMSDws2YHf28fgErEl6k+/8MIL5rUlrxHxStr3PEEYyJyTXBfTageRQI5lYPKEHGC8pA8++KBdV5588kmb6MPA53cihWOTaAmMWjyuDJLJ8Y/0/xaLtvxqJxGOqXC88sorFtmQGyZgI4Xr9549e0zkDE8xE0dnnHGGCc5xnJPnjKAeuhPkxCdrv2IlKJvVY92v+5+f9z6R/EQ/9SuE8B0GhswQe6GUhFUxg4y3Jifw0UcfmYgPoVjcxBgEcyNDjCReMDBAuMm7ATNYJ5wzI1GwRAWDHu8txxAL4WoMaLzX3kJodkYGN2AEMZhi0IA3hkkSjBs86gxcooGBE6HSTCJhSKEGS1tMFESrThyqCEtYK9vONrGwje3bt08Vmh2rdjhm8PighD5z5sywA0NCLFGtxSBFLCqa45OJuN9++8398MMPFlrO74SGUkcCIfkYt9Hi9/ZxTGEsM+Hxzjvv2HWOiY6JEydatA/n35gxY+yz6U120E6wsBJhycFh2ahee+syMrjZvyiFI+7G/51rFNvBZCeilHipIjVu/WrLzz7F6pgCri/8L8It0cC9wC/DMbPHejg4HrkG4HWnfzVr1jTF6tD0tFj0CUOTazjnC/977p+IoWLccg0m7SAe+N0vjkfOVY4lJpI4FmmTMQNh6lxfohGt8+tY9+v+5+e9T+QA4p1ULkR2gHCMJwTk1cdMpDqeiNEg5hQMQmGZqcWaiFAzF2GjYJ555pnAlVdeGbc+IXCyefPmI2oII8QVi9+KVCgus4wcOTJwzz33+CakFu67Bw4cCIwdOzZQqVIlE3cKrrkcbVvr168PPProo7a/EcDavXt3RG1RlxsxqPTEaaghm5HYkV/twJo1a6zOLaJnCAOxPRzbZ511lok2IjgVLUWKFDlCLItjiPXRQo3g+fPnBzKLX9uH8BnHQqtWreycaNy4cWD8+PFWHxjxrffeey9FoAlRu3B1qsMdUwjOcf3cvn27veYYffDBByPqk3cPaNKkSWD27Nkm8JTZc9avtvzsU6yOKeD66S0IXb3xxhsmjofIXTRwDHCvQ1wvq2T1WA/m/PPPD9x+++2B//77L9CoUaNAnz59Ar179466BnVW+kT9etixY4fV+163bp0JRXL9AkT14oHf/frxxx9NQJFxD9eaOnXqmNgf+71r167Zfqz7df/z894nkh8JqYlcATOlnnhJrEp1ZQVymymRQ0kJhIEIt8SbwQx5shJcE5twZnKaX3rpJcutIvSLPKvgciXZDR53xG2CSzAxI04YWDKCly/SGvOREC7zCI8EXs0bbrjBQuXwwFHeJ6PSWuHaopzTgAEDLASYNvFI4t2ItiRWuNBE/o+hAlyxbIeQafI28YhxXONVwYuLlzizAjnoOtDmI488krKO19HWrvWjtJNf23fttdda9A7XOqJ70DpIa99nJGoX7HXHK0UaBccjYm14uAjjjARCtlkWLlxoIc5sa/369e2axXZGkxLiV1t+9ilWxxSEnquEG1OvmTrb1G6OFEomedcEvIrBIo8IacWjjJmfpcyy0idCuYkMIdXl6quvtrQLynJRMg4BzXhliPrZL7zR3Lu8hTEa4eBcXzjWM1MmMavHul/3v1je+0TyIaNb5AqCL2aJeGHjok3uEfXDUfdFoATFzkhUmBOV0Bq71IdNJLjpMTBENRfFVW72THqkp6aaWbJSLztSyBO77777Uq1DVCozYFylJxxDaCETKF6YHGH66RlHXu5tOBDCIiS4U6dONnhLry1qm3o5rYT7eYJa3vPggQ6fIa83lu2EwoCaYykr9bA9CCNmQIvyMRNxDEQZ5BLKGS1MJBHeyiDxrrvuSmXURKMyndXtY7DKvmzatKmFkKLCzYCbSVEG7J7xTAgmpKVnwf8k+NgmNJW8c9pkf/Xt29cMJS8fND0IF2XxBB8xdDF42WeEuzJA5n2O94zwqy0/+5TeMcV+z+wxlRaE41J7OBq49jKxwMKkQlaum34d64AmBkY3xxsiXBxnH374YdRjiKz0CUOP/UB+MvWsAZVwrvUID3qaFNmNX/3i+k9aFOc+k2U1atSwXGpCu7MyaZKV66df9z8/730iZyAhNZEr4YLKjHXo4R9LoSuReJBTzuw3Ex3MqpNDiUfJ75Jo2SGkFgpGJKrQ0Q7Mve3Ay0MOXEZQbo/Bcnr9IAeOQUV6MEBBYAtDLBx4nXmvZ8+eZlR5njTygRFBC/1NBk7hcoz9aic7QFCRXGqOT44dBAkzU+otrTKIDJqzW6iRyTgmtjC0MLARNGRQTH59sJHFoD3ScjxMUDJh6YGXjAEs+ysj3njjjTTPAQx77hV4yxiwZ1dbfvYprWNq27ZtKceUl/+elWgm73whN59JFUS14oGfxzqTHcGlzDCgPEPOKzOY3X0C/v/ehBIK9p7RG28y0y8mNjKaXOH9aPO6MeQxjjNz/fTr/ufXvU/kHGR0i1wHodzUcOUGGnz4Z0YhU+RcMMpYIgmfjXdJND9LO2VHGazMwsQFHhDE4DyDIzPlXPxqR0SOX8eV38enp57MOYuRj6c2GO8egYczI/xqKxZ9CiU0CiAz977QUli0h/AbkQrZEd2TTKXM/IT/HSH5lStXdolMdt4bQuGcIdSdlBYmSPBKR0Ii3/9E8qMjReQ6yBEbOnSoeVK4YHqLDG4RzKhRo8wLF6n3Dg8UnnNCK1EqBfK1sgOOZ1RZI6k5TTgb+XfZ1dYTTzzhIgHPJB7L9EA9GLI6oPerHQ9CfyF4Eo9wRib2OC7iqV2QKNvnHVeRXGfTO678PD5hxIgRVjMcL5OXS8pkDOHbhMuOGzcuIuPWz7b87JOn/sxCiURCeNkvlHpCM4QySKjUZwbyr4MXQqXpY04xuCllFly9AEMu3gY3kNufkY5GvCGHPRLPLakq1PT2G+7DhPKjIcH1iRJnlCkkwiM9/Lq++H3vEzmEeCu5CZHdoCSK0qoQ6RGNUnChQoVMfTUYFH0LFy4cyC4GDBhgqrozZswI/PPPP0e8j9LqrbfeaqqrP//8c7a0hepyx44dTfF61apVYT+DIiy/hxL2t99+m+F2hqqOH3300dbP6dOnp1o8JetYt+OpnH/99de2P/73v//ZPkO9d/LkyfY8WrVjvwlXscFbIsGv7fPruPLzWA+F/zcqzBdffHHKPuJakBn8asuvdk488cTAxo0bU63jdenSpQOZ4emnn7bjIZhFixYFLrzwwkCyHuvBVKlSxdTj49WnUaNGBebOnWvPDx48GKhQoULKeyh7x5M5c+bYMm/ePFt4zvUSpfcbb7zR+rpkyZKI9s3evXtj3l/uLTfccENEx4Ef15dY3PtE8qPwcpHrIMdn8ODBJryDyqYQ4YgmD7tt27amQNq7d++UdUOGDHFLliyxkPPsgvyyp556yn4XDwIhdXgRqDeLKB9idniksrst8hYRrkPxFkEiry1CJPkd8ibxtoWG0YaD76E8T6inJyDH97wQP25peCk4x1u0aBHzdjylXLwVCCDi/UXBGU8keeJAbmAkIjlphQKHhgRHG5VD7rQHnumvvvrKPfPMM65Pnz4RqUz7tX1+Hld+tUMuOHnIqJ2zkGtOjjmCTmw3j0RGUB0gu9rys0/BEI7cq1evVBE4iEfiAWe/RQvh7wiEBQt+4mFEaAxvbDzI6rEeDLnbpAgR6VGoUKFs79Oll15qSvConxON5wmyeSJcwaJz2Q0aKAj6cd7znGsTXmSu8WPHjrV+r1+/3o7T9PYd1zwEFknvSY9XX3016j4SqbZgwQI3Z84cUxkn95pw80hqdft1ffHz3ieSHxndIlfAgCV4MEsOLoIyGFbBA4ZoxTpE7jW6Q0uiff7556506dJHlET74osvsrnnztfSVX62RRuUafPaYl9hQEQDpfQYcEYS/pcd7QD/Z1IKaIuQXUIOmdxjv6ELQCgowjzRDM7Tw48KDKiGMzCO5Pj0a/ticVxltR1vooNHQq8xTDEmQyc/Ihn0+9WWn30KDYdFGRojgusaYeuEmWNcZiT2FAzK/9CoUSObIPDEqRhOYqDyO1w7E4VojvVgMJZQu2ZckNVSZpnpE0Y5+xTjL9TQjrfR7V0TMCQxGuknIdVeahXnJaJzHGOIV6YFxzhh4Pny5Uv394YNGxZV/y644AIr88Z9GkOb0nvc06PFr+uUH/c+kfyoZJjIFZADJ0Q0ZJSXmMgl0fwsXeVnWwwUvXJQmYWcSkrSZBW/2gnm3nvvNUV0jA4G6c8//3xU38/OcoaZKe2U1e2LxXGV1XYwnvzKQfarLT/7FAzijhiQTJyQq8z5iNI44mfR4Hn46CMGkxcdwiPez7TU1+NFZo51v0uZZaZPRN9QvzkZYZII73K1atUy/CzlAv0uj3rPPffY8Z7VHHy/rlN+3PtE8iOjW+QKMhLTESKUjIKAIg2tFv5CaSkMhURpB6EeIh0I/2TgyEDvtddeM8ErBmoYOYRkI2iFeGPVqlUz9TsM+hm4+V3aKVG2L14QWp1obfnZp1A4hqKtVx0K/2vPyP722299N5iyQlaO9VA4zuPZJyZDMFy9dADCksM9p22MW6qyxBv69dBDD6VM7mB8ZzQ2y0o97rQILf8oRCIgo1sIIcKAN++kk06KdzdEAkO+bcOGDS3kEKVaQq0xPO+77z7L82/durUZ9wxEyXumJBlpLZHAoJwcanJw27RpY6rQDNbfffdd84BFQ2hURnBpp3htn0h+CB/m/59IZPZYT8Q+kf/L+eZFInjfC31OnjjXifr161uZrFiCMU0N7PQ8+ORwo5nj9TM9IlXhFyJHEG8lNyGEyE4WL15s6qpVq1YNFClSJHDsscea+jhKtV27dg3s3LkzkGz89ddfgWuuuSaVIvEPP/wQWLt2bcqyevVqU+xGhRol5IwYOXJk4N9//033Myi0VqxYMcO2UABG1Tajti666KJAMsE27d69O1C9evVA27ZtTfGWx/79+wcqVaoUePnll+1ztWrVirptlHHvv//+wJ9//mmv9+/fH3j44YcDjRs3DiTz9qF07LUdrAaMCjZqvnXr1s3WdoQ/cP7yv8jomiEiZ9u2bamuryVKlEilRB8M5+b48eNj3qf33nsvUKZMmZR+nXPOOVbFYNKkSUeccxwPp556arrt8f6+ffsCiQZ9f/bZZ8O+xzHepUuXwJ49eyJq55133knz/SFDhgQOHz6cpb6K5EF1uoUQuQZqhxKCh+cQVVFEXlCvRnV15MiRprZK3Vo8B8kEyq+EAXvcdttt7sorr3SdOnWy5yzU/cWrgBI3250RiPcQYv/OO++YJgJ5mqEL6tWRCJHdeeedpriNaNDixYtNiCl0ocY5OZTJBF4cRHXy58/vJk+e7IYPH27CS4R64plGXC+zIAJEmGaRIkXsNeI75FQH/58jBQV2QsCD4fjPKO0mFtuH1w/RNerm0ieOC4SO8OAjphSp6rVf7YjMs2/fPtOywCNbsGBBCzUvW7Zspo5Rv8jssZ6IfSJsHwVuj/S8xih0t2/f3sUa7ivcL9NTzkebgusVx0RGAmlc8xNRz5l9jZBdOIgq4DoYSRUJ2kGdPRzUDUfF/tdff81yf0VyoPByIUSuAUMSQ6FixYqp1mPYkHdHKC0h5Z988omFTiYL5FYyAH777bddnTp1bB0GG9tJSPIZZ5xhyuoDBw40QzySnFHy7MjH4zsoKE+aNMmUj6dOnWqTFt6jJ6KUHoSg0h5GY4kSJawMC/saQ7tBgwYpj5G0lYh4g0YEl5igoBQhpX2CBaeihdJQtMUAz2P8+PGZKnNIGwheBYcCYzhHqubs5/ZR9uiss86y45FySZQfo13C1SGjHFC/2xGZh4kPrjuvv/66/Q+4jpLr37lz57hUbfDjWE+kPhGqzf4FDLxQwz0YzoPshBJYzz77rBmMpGJxjfCMx6VLl6ZcyyNRDGdCOBYlw7JKuD6haM+EM2OESNXQw7XDdRRRSkqaRStkKJIXGd1CiFwDBh/GY1oz2NxIyU/FSE02GJBNmDDBPfbYY27dunVW/5Ma4Z9++ql5S/DeY3Cnte3BkEfogUFFFMDChQvtEYM5+JFyfJFCW3hkKIHEI4Zl8GM0bSUSDEAZHDNRQ+klPFgo8pJfiYGaGU8O9YGbN29udZTZL3iEKHOGOFG0pZ34fY6D0NJOkR7nfm4fhgTGB2WqvPJj5IDSHjnjkbblVzsi8yDyhXebnH+vzBkTMVyDshu/jvVE6hMTlZ7xil6CF/XitRFPiJii1jSeb/rC5C51xT2hPe5B11xzTUQ5/3j0M/KIx4PgSeCffvrJJo3x8vN/7dKlS8TX4uAJQMYX3INnzJhhxwA1zSm/JnIHMrqFELkGjEQGCRineLYxZrjZUzMVbysLIbTZWbrJDxj0EO5HKDiwbWvXrrVBj1d/F88k4eUMjKitm5biMEJYJ554ooWN430m5D7Ymxn6mB6E7uPRwQNCiJ1XhzozbSUyhFGyDRxb/fr1s3V4/BAcoyxZ8CRGpFD7moEr/ysGfIiqtWzZMiqlaL9KO8Vi+/wqP+ZnGTMRHRiNnseO44MFgyQz0RhZJRHLmPnRJ++aySSTl37D+RbvFKj06mYzAUw6SteuXd3o0aNTok+ys2SYHzCRwPWN8QJpUdQjpxQZ/z+8+dHcnxHGpB3OFyLESMFgm5nIfOWVV2K6HSJxkNEthMg14A1jdp4w3VWrVlmoOd4EDFaMQrzghK0mGwzCgj0FeI7Jd2Ubgw1gctoxgvGCT58+Pc1QOGbhKStEmOgjjzyS5by43r1720CcgUtOhPxrL/QwOI/dq91OWH5mYFCWldI3fpV28mv7/Co/ltPLmCULTFDi8USpmkgIjKutW7fa9SO7ScQyZn70ieiscNdoUomCIZopHtvM/ZQJhGDQWWBhEjujCexYlQzLCkR1sY+9smxoRYwZM8YmH4N1LLifcR1KKxWNfeO1g1I97TAZQWQI61jYdiZTaIf0GJHDibeSmxBCCH/o3LmzKbPDP//8Y4+oOffo0SOVyjMK1Hv37k23LU91FnVa1M5LlSplj2XLlk31mJE6bWhbqE6jfssj60Mfhf80a9YscPDgwZj/zrJly9JU4kU9P3/+/IHKlSsHBgwYEPjjjz+sWgCK9S+++GLguOOOCxQtWjSQN29eeyxYsGBM2xH+gLr+W2+9FXj66aft0VPbz+nHejz6tGXLFls2b95s1QM8uHbWrFkzwwoRseD444+3e8zy5cuPeO+mm24KdOzYMez3olW5z05VfJTZzzrrrECePHkCJ5xwglVvYGGdtxxzzDFW8SS9exbXIPYP7XDN4jkL1VK85aijjgrky5fPHkXOJw9/4m34CyFEdkCYc48ePcwLTF4xAi5NmzZNFVqNtyASVdJEBA/3qFGjTIEdkSm8f19++aXlju3YscNUqLnk4/VetmxZum2VK1fOZuAJHSWPLS2RM2b7PY9OWuDtoC28Bngh02qLz2TUVqKBJxURL+pY161b17zBRB2Ehszzmv/LJZdc4uIJuaHkWXIcRCNc99FHH1k6BsdFWvAeYZTkVofC75EbjgcMz/OaNWssPJnICyJPOC/xnhNWz/mZFn61I7IOYbGtWrVyJ598sktEMnusJ1KfPJFJwNPKdZ3vck3+5Zdf7J7FdYfopWDRxVhC5BL3kj///NNStn7++WfzyBPNxPlHlBS1vLleTJs2LdU91gPVbtJniJLISPSQ1JGnn37aNFeyE7aRfc7+J5XGy1kHUrBIS4sE7gekB5AiRDpMsEe7ZMmSbufOnTHpv0hA4m31CyFEdtGoUaPAhRdeGHjttdcCPXv2DBQrVixwxRVXpNTmphYys9LJxvr1622bTjrppJR1p512mnmWqe3K+9Ql/+KLL2wWf+nSpem2N2PGDGsvI+8CXs3g3wzHypUrA0899VSK5z29tkqXLh1Ixhrpzz//vHn+Z86cafudY6tkyZIpj9TXbt++vdWFj0cdY6Iabr/9duvL0UcfHfjmm28CJ598cuCrr76KuI0KFSqk1LavVq2atUdtZrYNjzO1uzmfMto+r5bvRx99ZG1NmTIlcOeddwY6dOgQVc1vv9oRmQdP3+zZs+PdDd+P9UTqE9dwoji+/vpr87B6nH322YHt27cHLrjgAjvus5PChQtbnWqWsWPHBnbt2mV1wt9++23ra506dQJXXnlloF27dum2w3WD73G/CXd/2LRpU+DWW28NNGzYMPDzzz8HshuuZ0QocC8888wzA5dcckngu+++Cxw6dMgiaiKFiJutW7faNZJ7XKdOnQK//vqrXT/xdovcg4xuIUSugZCw4Jv377//bgMDBkAff/yxGd3JGOaFcXfGGWfYYGj+/PmBoUOHmlFCuC2ha6tWrUoxUgiTSw/CdRls0B7fnzZtWprLxIkTbSCRXlvHHnts4NJLL7XBOSGIaS2fffZZ4LHHHgskK3v27LHJm+bNm9trb4DsPTJ4a9OmTWDfvn3Z3rdrrrnG/gdz5syxY4Tw1MGDBwfq1auX4Xf79OkTmDx5sp0jgFHNJA7HE8cABi/HHwuDy0iMCNixY0fg3XffDaxbt87SFBjIesZEJPjVjsg848ePD5x33nlxOaZjcawnYp84zt944w2b6GKCk++99NJLlu5zyimnBEaPHh3IbkJDqjG6vfsLk5Cce9xHSQXJiDVr1gRuvvlmm6zEqGVyEoOd62aDBg0C48aNC8QL7oMeXPe4H7LfR40aFXj//fcz1Q4TMA8++KAZ37QxcuRI3/stEhcZ3UKIXAOGwwcffHDEem7sGA9PPvlkUhrdXi4fA5XFixcHTj/99EC5cuUC9957r3lfGdzhZcHbQo4aj+mB4YKhhReC/cF+IyLg8ssvT7VgYDJQSg8mORgoMmCkreLFix+RH8eCFzU4TzFZYEDF/mFhGz2j25vcCPZOxQuObTwtgGFMXiielki8LHz+rrvuClx88cWp1mNAMNjGA+4RSU4+udgMPMlBZcKGiRm+5w3QI/VQ+9WOyDzsd4xuDJGHHnrIPJfekozHeqL1CU+4Z8xi9HF9YRKM85E2uZaOGDEikN2Ql4yX+4UXXgjs3r3brgNMCjABwPUOTy7HBusinZAh0mnjxo2Bzz//3CaJaTfesJ2hcH0hWiy9yeZQmGgJZdGiRXZPfvXVV7PcT5E8yOgWQuQa8IQxcJk+fXpYESgMp2Q0uj0Y8BCOh9eYgc+5554bOHDggInZNGnSJPDee++ZVxAPdaSMGTPGBoqI4tBWZuG7jzzyiIXl9e3bN03BrWRj27Ztgd9++y3wyy+/2KAx1OjOKLIgO2DSxYsiYJIAA2Dq1KkRedo8o5rPM7gGvMrly5c37z4CQY0bN7bUjQIFCmTYHkYyA/FHH300ZR2Dd4x6wi2JsIgEv9oRmeeWW24Ju3ANSsZjPdH6hHecSCUv/Sn4WoI3mYgTDFvSd7KTgQMHmtHZunVrm3CZO3euXQcQ8iSdyIP3szv03S8IdycVIBw//fSTjSMIO88IJksQVAvHt99+a5MnHAsidyCjWwiRq1iyZEmaN0u8D+TnJisY1G+++WaKV4UwRLzg5KFNmjQp0+0SDvncc8/50scvv/wycP/995vibU7By+Vm25i4uf76622iIvjRW+IB/SK3n5BGBoD169e355HklHrea4xvBohM5px//vmBJ554wtZ7g2483xnl96cFRjLRFTxG40GKVTsiecnKsZ6IfSLfmYgjQsw5/0hRwZhFQ8JLr+Cag15HPOBeyv0GD3UoeKxbtGgRyImwv4muifT+md69w7tfi5yP1MuFEOL/B+VslL5RPxbO6iAniupvorJ7925T0kaN9+KLLza1XtRqQ/ebpxqflbrbWQHF79mzZ5vqcdmyZV3Lli1dkSJFMvyeV/caJeKlS5e62267zb3//vvum2++MSVl7/3gz/rBV1995WrVqpXlGr5+tSOSh8we64naJ5TAqUWPQj/K4FxLbr/9drd+/Xp7f9y4cW7s2LHZpu6N4jbK3hmdU0WLFjV183r16rlkBXX44447Lur3ooH/Z2i1C5FDibfVL4QQ8YLwWLzDd999t+UTo1zueRCSEcJtUbP1FKTDzaDjBSQMMKNZekKJ0wqvixZ+i3DknApee46j4Px6xOEQtQPCzuPJyy+/bErHmcHzdHuPKFbfc8899og4HMeIl8+eVhhlKOgOkNeaHohEZZQP6lc7IueQlWM9UfuEQj/ecc430n3IB0Ys04PoDqoIZFedbs4pPOxc10jj4DnpNeSY85yFtJvrrrsucMcdd6TbFjXGIbjvqJZzTSUlyctpjxdooHA/JYUoFMYN3bt3j6gdT68kXHUH/neE7Iucj6Z+hRC5ynP7xRdfuAULFrh58+ZZrWrqi+JJoL7yNddck6qGZrJBLVA8e3hZqat8xRVXWB3j0qVLp3xm48aN5n148MEHXbFixdJsi1qywfVTqQF77LHHpqzjsUmTJlZ7OiP4LDVlg+v7hrZFO3hKk5UOHTqYN6tMmTLm7aUGLY8XXXSRa9Omjbv66qvdI488Epe+DRs2zJ1yyimZqqeMZ+6CCy6wmrR4uLt3725etnvuucdNnDjRji08bHhr8PhHwi233GLH6vbt221/nX/++W7EiBHu0Ucftbq4tLV//357nh3tiJxDVo71ROtTp06d3AknnGB1sLl+9uvXzx5ffPFFd8kll7iHHnoo5fp51VVXZZu3FA93qVKlzNPLfYLnwH3He+5dE4n6yegcJgKga9eu7oMPPrA2rr32WtezZ097/Pvvv112M2rUqJQ65GwjjyeddJJr1qyZ3Te5prN+ypQp9r9IC6LmvHa4jv7xxx+ucuXK7oYbbrC635UqVXKHDx92b775puvbt2+2bqOIDzK6hRC5BgYw+/btc40aNXJdunRxDRo0cBUqVDDjs3379q5cuXIumWHwVahQIbu533jjja5z586pDG747LPPLFS4fPny6baFUcziMXjwYBsseBAq/eSTT9pggv2aHgzMgtu6++67zUAKbotB19atW30J18tOHnjgAVe7du2UUEu2lXUY3Bh87B+OKwbMxYsXt23PbhjQPfHEE2YUc3xEA4PoIUOGuLVr17oxY8ZYCsaZZ57p+vfvbxNWPDJQjiRTjUmHGjVq2ECadpgEYz9xXM2aNcu9/PLLKfuH9tJKbfCrHZHzyMqxnmh9Ovfcc81oxXAjnYPzjoWJ0+OPPz7lfrVlyxZ36623uh9++MFlBz/99JP9NufWr7/+mtIP7jsYpBiWhJQTEp/RRAPXRNJUuE9dd911Ztgy8d22bVt7P3jiN7vg+s2EsjfZzHZiIDdu3NgmHPkfcF0kbYU+pwWT3qeffro959pEO15KAfc/1hGCT7j+HXfckU1bJ+KJjG4hRK5h6NChNvvMjDpGHgOD+vXr22zzv//+63IC3NgZgFWtWtUdOnTIcohfeumlFK/29OnTI8orxmsS7DnBcGFGPhiMbaIFLrvssnTb4rvBbTGQCs0/ZPCxZMmSDNtKNMjP7NGjhxmAeFwxAOvWrWvbuGvXrpR8xrPPPjtufdywYYM9MnDE+xQ8sYFXOC2YDOF4YhtYyCf99ttvbaDJYPSmm26yiQXe43Nsb3rgFWrXrp3lpnrHBX167LHHbJ9xjA4cONDemzx5cszbETmPzB7ridgnzxDjuvjxxx/bd4EJPiK1PO8oxi6TT9kFffF0K7jeecvBgwctaowoH867e++9173wwgvpGpREqjBR+dZbb7m5c+ea0c7ExKBBg8zbHQ/ZKYzgmTNnprxmmwoXLmxRPkxkM1k/Y8YM991336XbDhMJXC+D22Gicvjw4TZZeemll9pkyqpVqzQxmEuQ0S2EyDUgAsXCjfzrr7+2MHOM8H/++cdVr17dZp8Rw/LC9pIVQn+ZhSckGA8r24bAF4YtEw6Ed0dLuNBFhLXwavjRFv1jwJVsMBC76667Urz37HuMUzxRDIS9gXI82bx5s3mPWHbu3JmyPqNw1P/973+pIhKAY2nOnDk2WdW8eXMb8HsQNZKe+J4nrPT222/bABQvF2JQ7D8Gnjt27DAPE+cnz9PCr3ZEziOzx3oi9wljPfiehAeV6Jrgyc/XXnvNZReRpBQxKce5yLkZCRjoTCIQqcI18/nnn3fxgjShZ5991u5tXOOAMcLChQttMoBtIiwer/eHH36YpqAcIejvvPOOtcN1M3gS5qmnnrKJ2qZNm9o9mmuZyPlIvVwIkethhp4caAxwbqzBs9PJAIYOHmwMkN69e6ca2AFeB2boTzzxRAvbI389Pcgb/PTTT814xEDnNsFkxauvvprK88BzDDMGH+ntW/LMGbzgyaCv5CNi/Idri9n/ZIO+Y2CvXLnSdAFyGvzPMByCc+4JKw3O34SOHTuaFyetFIFgJXRU38l1JySWY6NPnz6WN/n666+nRGsE6wDEoh0hRPxYvny5XTNWrFhhRjqT4Ewe4PVm0ozQ7FatWpnXmagtoreyAybvMIhXr15thjPq8Hi3CX2nsgmTHlxbuI8xKUmETTj4PO3g+Scqh2sjoelsB+0wUYsxTxtMNBB9J3I2MrqFELkGwuIiIVJBqESBsG9m3slLx7ANNboRcmGg8Pnnn5tHEGMlLXgf4SxC0BkkeF4NcsEZeIQaYwwa8HiHgxxtZvLr1KnjJk2alJJfThh2aK651xbGXLLBZAcGKP3Pifi1fX6VH4tXGTMhhD8Qmn3OOeeYwBz6I3i6CbnmHGZiGM8+YeekrxBuzoQsBnB2wwQ1hj959K1bt071HvfEbt26mThrenDdZGKadBwmZvlOMNyzWff999/HZBtE4iCjWwiRa/Byi8m3JRfXI/gyyPvJNkjHqCafljBEvNkYzuSgcbNH/IqZeERg2GYGOxgoaUF+Ox4HwuzJP0N4DhBCY+ATDQyYmBDAm5HVthIZtov9jjcGmLAIVWcnVz100Jbbts8zgL3HM844w46zRYsWmbeLkHzyKb38cI7FWLYjhIgPnJuEcTPBjeeXKhucx3iEx48fbzoZeIPRwuCczk4QsIu0Djkh8WnpdTB5EEk73KuJyKlZs2aW+i0SHxndQohcA7PnzCZjeGL0kWeFCjILNzxu/hndIBMdjCPC1cg7I1T78ccft4WwZ4xwQvYI4csof5qwONrau3dvlg1lP9tKRDBG2T5vUgEjlDD/YEEyogYI0U6kUkbZvX383xFdw9tDygOlkLzyY3i98HQFlx8jUiKW7Qgh4gsG7pdffmlRaHiUR48ebZNnXHMI646H0U0U1rvvvmupUYjAMQnAtcRLkQKEV7mvcj9NK/+ciT+ui2wLgnlUAKEdwtc9XRXaocwh0WVPP/10tm6nyH6Se3QphBBRwE0v2AuL8c0NnfxPcowJZ0PshhC3ZAVPPbPv5JNRFoy8NE+whwkFcrsRicmMaBm57qHztIhnMVMfLRhwoW2RCxyPEjFZJZzSOwOyYCZMmGC5fclodPu1fX6VH/OzjJkQIn545yj3XYxszmN0GjwByngI4OGdZlKPcHbU1b0KFIwPvOdAuHh6quOExVPpgXaonOKVFyN3O7jUGNubU6qniPSRp1sIkWvAyPY83d5CaDYe7mCPd7AybLKBh5scXERqqEeOURJ8gyf0nIENed+Ex6UFkxLM1DPb73kXCxYsmDII4taB5xyvOUJW6cGgA0+p11ZoCTHa4vXEiRNT6rMmE5F48gml9Gq05sbtwxuOii91yz1Cy4+hwsyAN72wcL/aEULEHzzF3Kvw9F599dVmkKIBgnga96l4eLoxtCk9iM4I0TTeeIDcbVJrvDrk3F8xxNOCNBe2ifskFR/QVYGpU6da1J3XTmYqgIjkRJ5uIUSugXByL6ebMFRu9J6gF+t/+uknW5LV6MZ77IUAM3DBGxgqWMYNHmMbb3563m4MlWDDKivh4LQVbPgQDoxR5HkJMI54H89AskFZHEIj2T+eii2DtVBFW0+d/cknn3S5cfv8Kj/mdxkzIUT88CZyr7zySiudBQ888IC77777THvE05HITqjSEa4OOdokXh1youOY0CN8HCG4cJCC47XDeMNrBy0K2kHvhJQYtFYoRUZ4vcjZyOgWQuQaKIEV7GFlAE8eKoY2hiiGIF68ZIWZ9WDjGCGaYMhtZcKBkHO82BnN9r/00ku+9It9+uCDD6a8TsYQ63BQpoqJi549e9pgiigCj+DnwersuXX78IKTfx1afowUCLxJhIUHQ8kgvNqh5cf8akcIEX84ZyG4jCX3LXKpoV27dtneJ7zZkTB79ux0vdRE3mQEkwpjx449YnJc5EwUXi6EyDVQqorSJAzYEUoBwrzItyLfmRsf9a4JM09GCN+mbBjbRth8aPg4Bgj1yNk+xGoIw80sGGLUQ77//vt96Pn/hQ1j6CcbRAyguptT1dn93j6/yo/l9DJtQoj/IxEjVfy8/3HfxuMvcj7ydAshcg3UsiaEGaElQJX0xRdfdB9++KGFmhPehlGOYZpsEJKHIbRw4UJ73aJFCzOOCFsj7BYQi3vrrbfM40+edUZGN7PwTEawIPRC+xjGhMSR30beXUZ4+dpA7l5wqSngPQw6PB60G+pBTXTQACDnOR6CP8m4ffz/gysEZLb8mF/tCCESl+eee85NmjQpw1rYsSCr97+cfu8T0SOjWwiRa+BGSchsmTJl7CaKyvfDDz9sZYWAUh7c5JMNyqzMmzfPDG7EyGDkyJFmYLds2dIEXyhrgiANuXJ9+vRxp5xySobtIiaD8Jw3gEBMi1JMqEWT58uEBUZNxYoV02wDY4g+McAgVJk2ly5dagIyPCKWQ7g7ff31119dTvHMMCALDSRj23NCaH1Wtg/hoWDxoSlTphxRfoxoDfK202vLr3aEEPHDK8HlebK5tmDcIjrG5PCSJUtscjweZPX+lxvvfSIDCC8XQojcQJ06dQKvv/66PX/iiScCRYsWDezduzfl/UmTJgVOP/30QLKxa9euwPbt2+35iSeemOq9Tz/9NFClSpVAvnz5AqVKlQrMnj073bZ+++23wL333mttnnXWWYFDhw7Z8tdffwVKlCiR8rl9+/YFrr/++kC5cuXSbS9PnjyBNm3aBP777z9rDypUqJDq8cILLwxMmDAhcPzxxweSEfZF/vz5U15Xrlw5UKNGjUCtWrVsqVmzZuDMM8/McN/nhu3jWAr+P+fNm/eIz5x88smBxYsXZ0s7Qoj4UbZs2UCjRo0ChQsXtmtItWrV7DmMGTMm8OuvvwaWLFli16DswM/7X26494nokKdbCJFrGDFihLvqqqtMqIWZddRDER/btGmT69Spk/v888+tBFayQR76p59+arPxlHW69NJL3Y4dO0w4jVyx8847z7wGePEzyoEl7539UalSJfss6qsewSHGCFNRm7latWrptudFFRDa730/9JFwfhTNEy1vL1LwzASrs6Num5OI5faFC1snGiXa8mp+tSOEyD6IQiG9Cy8wYpvcw9BYgdtuu83uQY8++qjdlzifY42f97/ccO8T0SGjWwiRayC3mdAwbt6EVxMuBuRpUa7jhRdecGeddZZLNhgAoN7MdjBI6N69u93wKUVSrFixlM9hjJPrTXklSpiEA5G1WbNmud9++82de+65R7xP+B+TFvzeI488kipHLRyEAFNepVy5chbyS11WBhnBj0CYf3CObjLBxA3lbXIqfm2fX+XHcnqZNiHE/8H9BdHPjCZ3/cLP+19uuPeJ6NB/WQiRq8AwDa3DzWy7Xyrc8eC6665Lef7MM8+4Zs2a2fOnnnrKxNKKFy9uj0w04EW44YYbTLwlPcVUJiQwtkKNmMcff9xm5THsISOBLb6DB8Ob8CCHr2HDhm7BggUpj4BYVzKql3uDq6ZNm7qcih/b51f5sZxepk0I4dz+/futsgheZ+4ZGU3u+o0f97/ccO8T0SGjWwghciiosRNWTqknSix99dVXJjZ1+eWX240+LaMbQ2Xq1KlHrGeAgbETHHLnlZFKj86dO5vYDAIyeAgw4oIfPWMqo9rhInmpWrWqW7x4sZUfe+KJJyy1A6i57T3PznaEEPEDY5p7UlpghFJp5OWXX7bX2Vkdws/7n+59IhgZ3UIIkYMIVZQeO3ZsyvPhw4db6BxK52mxe/duU44lFy3cgGLLli2ucePGNvDAcGdglBaE9tKOF7JPyN7gwYOthnjwozegwaASORe/yo/l9DJtQuR0vv32WzNI8+fPH/Z9PNvevYF7Gp7v7MCv+5/ufSIcMrqFECIHEWyIhBolDBaoR54ezMATnk6eGWH4Xhkmz5g/7bTTrGwKngg+m55QFb9PWTYPapEyEOnVq5d9l9JleBUQ6UIIrl27dpnebpG7y6vl9DJtQuQkrrzySlvC5U174AnetWuXndsVKlTIln75df/TvU+EIw8S5mHfEUIIkXQgyOJd1nk89thjTWitdOnSNuuOevu1114bkXDLqFGjbEaeQQKDA76Dyvs111zjmjdvbu1EMhjyVGfTms3HC4CwGzVR6avIueCxIl8SYSFATIgBrKfeyzHLYBQvEKJ/sW5HCBE/qlSpYsJkVNZo1KiRnbeIJOJRpgKHdz4jCMp9KDvx4/6ne58IRka3EELkIP744w+bZfe83ITAETKHyNSSJUvcxIkTTSDm66+/TpWbFimEzRGejgI6ebQvvvhiurP05OT16NHDcuFQe0WABkVXJgM8Dh486EqUKOE2bNiQya0WyXR8kr+YVYEzv9oRQsSPBx54wO5RhJNjjuDV5h4zZswYK+FJeUKM3FBRs3gRzf1P9z4RioxuIYTIRTBoYMBAaF9WYcCAJyKtvDwYMmSIhek1adLEXt91112uQYMGrn379imf4TZ03333WZ7f7NmzE2aAJfwHjxF1ebOqhu5XO0KIxASP96BBgyz8evTo0a5t27Yukcjo/qd7nwhFRrcQQohsA+9FqVKlLDcuGPJx16xZYyVWhBBC5B7w9FaqVCnse5999pnld5cpU8YlM7r3if9LlhBCCCFihKfgCuedd57VLN27d2/KOvJyX3nlFTdy5Mg49VAIIUS8wCNM9AppT+HCtB999FGXjOjeJ4KR0S2EECKmkFPu8eyzz7rrr7/ebdu2LWUdAliUMgv+nBBCiJwLyuCPPfaY69atmxnclOhCVKx69eqW+0wgLurl7777rrvuuutcMqJ7nwhG4eVCCCFiCuF0GzdutLIpDKAoyYLaNHl6niItYjqAaq0QQoicDeW2br75Znt+9tlnm84I+c94tYcPH24VCsqWLWuGKQKgyYjufSIY1ekWQggRM1CjZUFNncEG4jgo1iIyg+orqrXA+5SAEkIIkfMpXry4Gdcev//+u1XUoBRXy5YtTXSMfOdVq1a5ZET3PhGKjG4hhBAxYcWKFe6OO+6w8k6EDzLg4JGQwYULF1rpFa+0GUFXhw4dSlqPhhBCiMjhPnDvvfeaQXrTTTfZugMHDli5sOeff97Vr1/fvN+oli9dutQVKlTIJQu694lwKKdbCCFETNi5c6fr2rWrlUGpWrWqe+uttyyk7s8//zQ12nr16qUs5557rj0KIYTIHTnd1KjGA3zGGWe4r776ym3atMmtXLnSjR8/3kKw+/Xr56pVq2b1rpMJ3ftEOJTTLYQQIqZUqFDBBh3M9teqVctCBvFkvPPOO5azV6NGDQsnJNxQCCFE7mLcuHHu4osvdj///LN5uIOhhnXPnj2tjnWyoXufCEZGtxBCiJgL5uzZs8fy2/BYPPfcc+6DDz5w3333nYXVLVu2zMLtRowY4dq3bx/v7gohhIgxJUuWdPnz53d586af6Vq0aFErqZWM3mDd+0QwMrqFEELElIcfftg9+eSTKYOrRYsWmVeDHDdPwZXBB8qu8+fPd3Xq1Ilzj4UQQsSScuXK2XWfPG68vYsXL7b8ZmpbY5TCP//847p3727GK/Wskw3d+0QwyukWQggRMyiXMmXKFAsbhO3bt1vO3tatW13p0qUtrw+xmbp167rp06db6RghhBA5GwzRUqVK2YIByuNJJ53kjjrqqJT1ZcqUcR06dEjKOt2694lQZHQLIYSIGbfeeqtr0aKFiccQYnfDDTfYzD5ejlmzZrn169dbLVO8GajYMuASQgiRs/npp5/sPoCI2po1a+w5C4boRRdd5Dp16mRiauRCU7872dC9T4Si8HIhhBAxY/Lkye6qq66y+qvTpk1zXbp0cevWrTNVV4/Vq1e7u+++21ReR40aFdf+CiGEiD2ff/653RcwNr2a1iwHDx60mt3cJwjHXrJkiXvhhResBFcyoXufCEVGtxBCiGyDwVQ4pVYGW+T2BQ9IhBBC5G5WrVpl9wxCzZMZ3fuEjG4hhBBCCCGEECJGKIFACCGEEEIIIYSIETK6hRBCCCGEEEKIGCGjWwghhBBCCCGEiBEyuoUQQgghhBBCiBgho1sIIYQQQgghhIgRMrqFEEIIIYQQQogYIaNbCCGEEEIIIYRwseH/AxbPYFsk6XqDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(te_corr, cmap=sns.diverging_palette(h_neg=20, h_pos=220), center=0)\n",
    "plt.gcf().set_size_inches((10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cd2a0a7310>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(tezheng.sample(frac=0.05),vars=['return_s1','股息率(%)','市盈率(静态)'])\n",
    "g.map_upper(sns.scatterplot)\n",
    "g.map_lower(sns.kdeplot)\n",
    "g.map_diag(sns.kdeplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quant",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
